Analyses (09/23/24)

Author

Nicholas Vietto

pacman::p_load(tidyverse, janitor, ppcor, ltm, pastecs, corrtable, psych)

options(scipen=999)

data <- read.csv("data2.csv") 

data <- data[, -1]  # Removed x column that appears when exporting a csv from R

Recodes and Scales

Dummy Codes

These numbers contain the missing values, to get sample descriptive (e.g., race/ethnicity) the numbers must be calculated with the data frames in the Wrangling section. in other words, run all chunks then run tabyl(FSFSurveyT1$race_eth) in the console.

# Sex

data$Genderfactor <-as.factor(data$Gender)
data$GenderNumb <-as.numeric(data$Genderfactor)

table(data$GenderNumb)

 1  2 
80 26 
data$Female <- data$GenderNumb
data$Female = ifelse(data$GenderNumb == 1, 1, data$Female)
data$Female = ifelse(data$GenderNumb == 2, 0, data$Female)
table(data$Female)

 0  1 
26 80 
table(data$GenderNumb)

 1  2 
80 26 
data$Male <- data$GenderNumb
data$Male = ifelse(data$GenderNumb == 1, 0, data$Male)
data$Male = ifelse(data$GenderNumb == 2, 1, data$Male)
table(data$Male)

 0  1 
80 26 
# Race

# Dem percents 
table(data$race_eth)

American Indian/Native American Asian or Pacific Islander       
                              1                               6 
Black/African American          Hispanic                        
                             10                              26 
Multiracial                     Other                           
                              4                               1 
White                           
                             58 
data$race_eth1 <- as.factor(data$race_eth)   
data$race_eth2 <- as.numeric(data$race_eth1)
table(data$race_eth2)

 1  2  3  4  5  6  7 
 1  6 10 26  4  1 58 
data$race_ethDC <- data$race_eth2
data$race_ethDC = ifelse(data$race_eth2 == 7, 8, data$race_ethDC)
data$race_ethDC = ifelse(data$race_eth2 == 6, 0, data$race_ethDC)
data$race_ethDC = ifelse(data$race_eth2 == 5, 0, data$race_ethDC)
data$race_ethDC = ifelse(data$race_eth2 == 4, 0, data$race_ethDC)
data$race_ethDC = ifelse(data$race_eth2 == 3, 0, data$race_ethDC)
data$race_ethDC = ifelse(data$race_eth2 == 2, 0, data$race_ethDC)
data$race_ethDC = ifelse(data$race_eth2 == 1, 0, data$race_ethDC)
table(data$race_ethDC)

 0  8 
48 58 
data$White = data$race_ethDC
data$White <- as.factor(data$White)
data$White = ifelse(data$race_ethDC == 8, 1, data$White)
data$White = ifelse(data$race_ethDC == 0, 0, data$White)
table(data$White)

 0  1 
48 58 

SRP Reverse Codes

IPM (16, 24, 31, 38, 61) CA (11, 19, 23, 26, 44) ELS (14, 22, 25, 36, 47) ASB (5, 6, 18, 21, 34, 46)

Citation: Paulhus, D.L., Neumann, C. S., & Hare, R.D. (in press). Manual for the Self-Report Psychopathy scale 4th edition. Toronto: Multi-Health Systems.

Code Reference

# IPM

table(data$SRP_16n)

 1  2  3  4  5 
 2 18 36 33 17 
data$SRP16nRev = data$SRP_16n
data$SRP16nRev = ifelse(data$SRP_16n == 1, 5, data$SRP16nRev)
data$SRP16nRev = ifelse(data$SRP_16n == 2, 4, data$SRP16nRev)
data$SRP16nRev = ifelse(data$SRP_16n == 4, 2, data$SRP16nRev)
data$SRP16nRev = ifelse(data$SRP_16n == 5, 1, data$SRP16nRev)
table(data$SRP16nRev)

 1  2  3  4  5 
17 33 36 18  2 
table(data$SRP_24n)

 1  2  3  4  5 
 5 19 24 46 12 
data$SRP24nRev = data$SRP_24n
data$SRP24nRev = ifelse(data$SRP_24n == 1, 5, data$SRP24nRev)
data$SRP24nRev = ifelse(data$SRP_24n == 2, 4, data$SRP24nRev)
data$SRP24nRev = ifelse(data$SRP_24n == 4, 2, data$SRP24nRev)
data$SRP24nRev = ifelse(data$SRP_24n == 5, 1, data$SRP24nRev)
table(data$SRP24nRev)

 1  2  3  4  5 
12 46 24 19  5 
table(data$SRP_31n)

 1  2  3  4  5 
 6 15 46 30  9 
data$SRP31nRev = data$SRP_31n
data$SRP31nRev = ifelse(data$SRP_31n == 1, 5, data$SRP31nRev)
data$SRP31nRev = ifelse(data$SRP_31n == 2, 4, data$SRP31nRev)
data$SRP31nRev = ifelse(data$SRP_31n == 4, 2, data$SRP31nRev)
data$SRP31nRev = ifelse(data$SRP_31n == 5, 1, data$SRP31nRev)
table(data$SRP31nRev)

 1  2  3  4  5 
 9 30 46 15  6 
table(data$SRP_38n)

 1  2  3  4  5 
 4 18 28 36 20 
data$SRP38nRev = data$SRP_38n
data$SRP38nRev = ifelse(data$SRP_38n == 1, 5, data$SRP38nRev)
data$SRP38nRev = ifelse(data$SRP_38n == 2, 4, data$SRP38nRev)
data$SRP38nRev = ifelse(data$SRP_38n == 4, 2, data$SRP38nRev)
data$SRP38nRev = ifelse(data$SRP_38n == 5, 1, data$SRP38nRev)
table(data$SRP38nRev)

 1  2  3  4  5 
20 36 28 18  4 
table(data$SRP_61n)

 1  2  3  4  5 
 5 14 15 37 35 
data$SRP61nRev = data$SRP_61n
data$SRP61nRev = ifelse(data$SRP_61n == 1, 5, data$SRP61nRev)
data$SRP61nRev = ifelse(data$SRP_61n == 2, 4, data$SRP61nRev)
data$SRP61nRev = ifelse(data$SRP_61n == 4, 2, data$SRP61nRev)
data$SRP61nRev = ifelse(data$SRP_61n == 5, 1, data$SRP61nRev)
table(data$SRP61nRev)

 1  2  3  4  5 
35 37 15 14  5 
# CA

table(data$SRP_11n)

 1  2  3  4  5 
 2  7 10 43 44 
data$SRP11nRev = data$SRP_11n
data$SRP11nRev = ifelse(data$SRP_11n == 1, 5, data$SRP11nRev)
data$SRP11nRev = ifelse(data$SRP_11n == 2, 4, data$SRP11nRev)
data$SRP11nRev = ifelse(data$SRP_11n == 4, 2, data$SRP11nRev)
data$SRP11nRev = ifelse(data$SRP_11n == 5, 1, data$SRP11nRev)
table(data$SRP11nRev)

 1  2  3  4  5 
44 43 10  7  2 
table(data$SRP_19n)

 2  3  4  5 
 8 14 54 29 
data$SRP19nRev = data$SRP_19n
data$SRP19nRev = ifelse(data$SRP_19n == 1, 5, data$SRP19nRev)
data$SRP19nRev = ifelse(data$SRP_19n == 2, 4, data$SRP19nRev)
data$SRP19nRev = ifelse(data$SRP_19n == 4, 2, data$SRP19nRev)
data$SRP19nRev = ifelse(data$SRP_19n == 5, 1, data$SRP19nRev)
table(data$SRP19nRev)

 1  2  3  4 
29 54 14  8 
table(data$SRP_23n)

 1  2  3  4  5 
28 37 15 14 12 
data$SRP23nRev = data$SRP_23n
data$SRP23nRev = ifelse(data$SRP_23n == 1, 5, data$SRP23nRev)
data$SRP23nRev = ifelse(data$SRP_23n == 2, 4, data$SRP23nRev)
data$SRP23nRev = ifelse(data$SRP_23n == 4, 2, data$SRP23nRev)
data$SRP23nRev = ifelse(data$SRP_23n == 5, 1, data$SRP23nRev)
table(data$SRP23nRev)

 1  2  3  4  5 
12 14 15 37 28 
table(data$SRP_26n)

 2  3  4  5 
 6 25 49 26 
data$SRP26nRev = data$SRP_26n
data$SRP26nRev = ifelse(data$SRP_26n == 1, 5, data$SRP26nRev)
data$SRP26nRev = ifelse(data$SRP_26n == 2, 4, data$SRP26nRev)
data$SRP26nRev = ifelse(data$SRP_26n == 4, 2, data$SRP26nRev)
data$SRP26nRev = ifelse(data$SRP_26n == 5, 1, data$SRP26nRev)
table(data$SRP26nRev)

 1  2  3  4 
26 49 25  6 
table(data$SRP_44n)

 1  2  3  4  5 
 1  6 20 53 26 
data$SRP44nRev = data$SRP_44n
data$SRP44nRev = ifelse(data$SRP_44n == 1, 5, data$SRP44nRev)
data$SRP44nRev = ifelse(data$SRP_44n == 2, 4, data$SRP44nRev)
data$SRP44nRev = ifelse(data$SRP_44n == 4, 2, data$SRP44nRev)
data$SRP44nRev = ifelse(data$SRP_44n == 5, 1, data$SRP44nRev)
table(data$SRP44nRev)

 1  2  3  4  5 
26 53 20  6  1 
# ELS

table(data$SRP_14n)

 1  2  3  4  5 
 8 13 17 40 27 
data$SRP14nRev = data$SRP_14n
data$SRP14nRev = ifelse(data$SRP_14n == 1, 5, data$SRP14nRev)
data$SRP14nRev = ifelse(data$SRP_14n == 2, 4, data$SRP14nRev)
data$SRP14nRev = ifelse(data$SRP_14n == 4, 2, data$SRP14nRev)
data$SRP14nRev = ifelse(data$SRP_14n == 5, 1, data$SRP14nRev)
table(data$SRP14nRev)

 1  2  3  4  5 
27 40 17 13  8 
table(data$SRP_22n)

 1  2  3  4  5 
 5 29 15 35 22 
data$SRP22nRev = data$SRP_22n
data$SRP22nRev = ifelse(data$SRP_22n == 1, 5, data$SRP22nRev)
data$SRP22nRev = ifelse(data$SRP_22n == 2, 4, data$SRP22nRev)
data$SRP22nRev = ifelse(data$SRP_22n == 4, 2, data$SRP22nRev)
data$SRP22nRev = ifelse(data$SRP_22n == 5, 1, data$SRP22nRev)
table(data$SRP22nRev)

 1  2  3  4  5 
22 35 15 29  5 
table(data$SRP_25n)

 1  2  3  4  5 
 9 35 37 16  9 
data$SRP25nRev = data$SRP_25n
data$SRP25nRev = ifelse(data$SRP_25n == 1, 5, data$SRP25nRev)
data$SRP25nRev = ifelse(data$SRP_25n == 2, 4, data$SRP25nRev)
data$SRP25nRev = ifelse(data$SRP_25n == 4, 2, data$SRP25nRev)
data$SRP25nRev = ifelse(data$SRP_25n == 5, 1, data$SRP25nRev)
table(data$SRP25nRev)

 1  2  3  4  5 
 9 16 37 35  9 
table(data$SRP_36n)

 1  2  3  4  5 
 8 21 15 25 37 
data$SRP36nRev = data$SRP_36n
data$SRP36nRev = ifelse(data$SRP_36n == 1, 5, data$SRP36nRev)
data$SRP36nRev = ifelse(data$SRP_36n == 2, 4, data$SRP36nRev)
data$SRP36nRev = ifelse(data$SRP_36n == 4, 2, data$SRP36nRev)
data$SRP36nRev = ifelse(data$SRP_36n == 5, 1, data$SRP36nRev)
table(data$SRP36nRev)

 1  2  3  4  5 
37 25 15 21  8 
table(data$SRP_47n)

 1  2  3  4  5 
 8 54 26 13  5 
data$SRP47nRev = data$SRP_47n
data$SRP47nRev = ifelse(data$SRP_47n == 1, 5, data$SRP47nRev)
data$SRP47nRev = ifelse(data$SRP_47n == 2, 4, data$SRP47nRev)
data$SRP47nRev = ifelse(data$SRP_47n == 4, 2, data$SRP47nRev)
data$SRP47nRev = ifelse(data$SRP_47n == 5, 1, data$SRP47nRev)
table(data$SRP47nRev)

 1  2  3  4  5 
 5 13 26 54  8 
# ASB

table(data$SRP_05n)

 1  2  3  4  5 
15  8  2 16 65 
data$SRP5nRev = data$SRP_05n
data$SRP5nRev = ifelse(data$SRP_05n == 1, 5, data$SRP5nRev)
data$SRP5nRev = ifelse(data$SRP_05n == 2, 4, data$SRP5nRev)
data$SRP5nRev = ifelse(data$SRP_05n == 4, 2, data$SRP5nRev)
data$SRP5nRev = ifelse(data$SRP_05n == 5, 1, data$SRP5nRev)
table(data$SRP5nRev)

 1  2  3  4  5 
65 16  2  8 15 
table(data$SRP_06n)

 1  2  4  5 
10  4 17 75 
data$SRP6nRev = data$SRP_06n
data$SRP6nRev = ifelse(data$SRP_06n == 1, 5, data$SRP6nRev)
data$SRP6nRev = ifelse(data$SRP_06n == 2, 4, data$SRP6nRev)
data$SRP6nRev = ifelse(data$SRP_06n == 4, 2, data$SRP6nRev)
data$SRP6nRev = ifelse(data$SRP_06n == 5, 1, data$SRP6nRev)
table(data$SRP6nRev)

 1  2  4  5 
75 17  4 10 
table(data$SRP_18n)

 1  2  4  5 
 6  1 12 87 
data$SRP18nRev = data$SRP_18n
data$SRP18nRev = ifelse(data$SRP_18n == 1, 5, data$SRP18nRev)
data$SRP18nRev = ifelse(data$SRP_18n == 2, 4, data$SRP18nRev)
data$SRP18nRev = ifelse(data$SRP_18n == 4, 2, data$SRP18nRev)
data$SRP18nRev = ifelse(data$SRP_18n == 5, 1, data$SRP18nRev)
table(data$SRP18nRev)

 1  2  4  5 
87 12  1  6 
table(data$SRP_21n)

 1  2  3  4  5 
 6 13  8 24 55 
data$SRP21nRev = data$SRP_21n
data$SRP21nRev = ifelse(data$SRP_21n == 1, 5, data$SRP21nRev)
data$SRP21nRev = ifelse(data$SRP_21n == 2, 4, data$SRP21nRev)
data$SRP21nRev = ifelse(data$SRP_21n == 4, 2, data$SRP21nRev)
data$SRP21nRev = ifelse(data$SRP_21n == 5, 1, data$SRP21nRev)
table(data$SRP21nRev)

 1  2  3  4  5 
55 24  8 13  6 
table(data$SRP_34n)

 1  2  3  4  5 
 3  4  1 13 85 
data$SRP34nRev = data$SRP_34n
data$SRP34nRev = ifelse(data$SRP_34n == 1, 5, data$SRP34nRev)
data$SRP34nRev = ifelse(data$SRP_34n == 2, 4, data$SRP34nRev)
data$SRP34nRev = ifelse(data$SRP_34n == 4, 2, data$SRP34nRev)
data$SRP34nRev = ifelse(data$SRP_34n == 5, 1, data$SRP34nRev)
table(data$SRP34nRev)

 1  2  3  4  5 
85 13  1  4  3 
table(data$SRP_46n)

 1  2  3  4  5 
18 21  2 16 49 
data$SRP46nRev = data$SRP_46n
data$SRP46nRev = ifelse(data$SRP_46n == 1, 5, data$SRP46nRev)
data$SRP46nRev = ifelse(data$SRP_46n == 2, 4, data$SRP46nRev)
data$SRP46nRev = ifelse(data$SRP_46n == 4, 2, data$SRP46nRev)
data$SRP46nRev = ifelse(data$SRP_46n == 5, 1, data$SRP46nRev)
table(data$SRP46nRev)

 1  2  3  4  5 
49 16  2 21 18 

Levenson Reverse Codes

(3, 7, 10, 13, 15, 21, 26)

Bold missing from half of surveys

See code book to match the questions in figure to the numeric values in survey.

Citation: Levenson, M. R., Kiehl, K. A., & Fitzpatrick, C. M. (1995). Assessing psychopathic attributes in a noninstitutionalized population. Journal of Personality and Social Psychology, 68(1), 151–158.

table(data$Lev_10n)

 1  2  3  4 
 5 23 52 26 
data$Lev_10nRev = data$Lev_10n
data$Lev_10nRev = ifelse(data$Lev_10n == 1, 4, data$Lev_10nRev)
data$Lev_10nRev = ifelse(data$Lev_10n == 2, 3, data$Lev_10nRev)
data$Lev_10nRev = ifelse(data$Lev_10n == 3, 2, data$Lev_10nRev)
data$Lev_10nRev = ifelse(data$Lev_10n == 4, 1, data$Lev_10nRev)
table(data$Lev_10nRev)

 1  2  3  4 
26 52 23  5 
table(data$Lev_13n)

 1  2  3  4 
 3  9 41 53 
data$Lev_13nRev = data$Lev_12n
data$Lev_13nRev = ifelse(data$Lev_13n == 1, 4, data$Lev_13nRev)
data$Lev_13nRev = ifelse(data$Lev_13n == 2, 3, data$Lev_13nRev)
data$Lev_13nRev = ifelse(data$Lev_13n == 3, 2, data$Lev_13nRev)
data$Lev_13nRev = ifelse(data$Lev_13n == 4, 1, data$Lev_13nRev)
table(data$Lev_13nRev)

 1  2  3  4 
53 41  9  3 
table(data$Lev_15n)

 1  2  3  4 
 3  8 32 17 
data$Lev_15nRev = data$Lev_15n
data$Lev_15nRev = ifelse(data$Lev_15n == 1, 4, data$Lev_15nRev)
data$Lev_15nRev = ifelse(data$Lev_15n == 2, 3, data$Lev_15nRev)
data$Lev_15nRev = ifelse(data$Lev_15n == 3, 2, data$Lev_15nRev)
data$Lev_15nRev = ifelse(data$Lev_15n == 4, 1, data$Lev_15nRev)
table(data$Lev_15nRev)

 1  2  3  4 
17 32  8  3 
table(data$Lev_21n)

 1  2  3  4 
 1  6 46 53 
data$Lev_21nRev = data$Lev_21n
data$Lev_21nRev = ifelse(data$Lev_21n == 1, 4, data$Lev_21nRev)
data$Lev_21nRev = ifelse(data$Lev_21n == 2, 3, data$Lev_21nRev)
data$Lev_21nRev = ifelse(data$Lev_21n == 3, 2, data$Lev_21nRev)
data$Lev_21nRev = ifelse(data$Lev_21n == 4, 1, data$Lev_21nRev)
table(data$Lev_21nRev)

 1  2  3  4 
53 46  6  1 
table(data$Lev_26n)

 1  2  3  4 
 6  3 54 43 
data$Lev_26nRev = data$Lev_26n
data$Lev_26nRev = ifelse(data$Lev_26n == 1, 4, data$Lev_26nRev)
data$Lev_26nRev = ifelse(data$Lev_26n == 2, 3, data$Lev_26nRev)
data$Lev_26nRev = ifelse(data$Lev_26n == 3, 2, data$Lev_26nRev)
data$Lev_26nRev = ifelse(data$Lev_26n == 4, 1, data$Lev_26nRev)
table(data$Lev_26nRev)

 1  2  3  4 
43 54  3  6 
table(data$Lev_03n)

 1  2  3  4 
 1 12 56 37 
data$Lev_03nRev = data$Lev_03n
data$Lev_03nRev = ifelse(data$Lev_03n == 1, 4, data$Lev_03nRev)
data$Lev_03nRev = ifelse(data$Lev_03n == 2, 3, data$Lev_03nRev)
data$Lev_03nRev = ifelse(data$Lev_03n == 3, 2, data$Lev_03nRev)
data$Lev_03nRev = ifelse(data$Lev_03n == 4, 1, data$Lev_03nRev)
table(data$Lev_03nRev)

 1  2  3  4 
37 56 12  1 
table(data$Lev_07n)

 1  2  3  4 
 1 24 56 24 
data$Lev_07nRev = data$Lev_07n
data$Lev_07nRev = ifelse(data$Lev_07n == 1, 4, data$Lev_07nRev)
data$Lev_07nRev = ifelse(data$Lev_07n == 2, 3, data$Lev_07nRev)
data$Lev_07nRev = ifelse(data$Lev_07n == 3, 2, data$Lev_07nRev)
data$Lev_07nRev = ifelse(data$Lev_07n == 4, 1, data$Lev_07nRev)
table(data$Lev_07nRev)

 1  2  3  4 
24 56 24  1 

ICU

(1, 3, 5, 8, 13, 14, 15, 16, 17, 19, 23, 24)

Essau, C. A., Sasagawa, S., & Frick, P. J. (2006). Callous-unemotional traits in a community sample of adolescents. Assessment, 13(4), 454-469.

Code Reference

# Callous

table(data$ICU_8n)

 2  3  4 
16 56 34 
data$ICU_8nRev = data$ICU_8n
data$ICU_8nRev = ifelse(data$ICU_8n == 1, 4, data$ICU_8nRev)
data$ICU_8nRev = ifelse(data$ICU_8n == 2, 3, data$ICU_8nRev)
data$ICU_8nRev = ifelse(data$ICU_8n == 3, 2, data$ICU_8nRev)
data$ICU_8nRev = ifelse(data$ICU_8n == 4, 1, data$ICU_8nRev)
table(data$ICU_8nRev)

 1  2  3 
34 56 16 
# Uncaring 

table(data$ICU_15n)

 1  2  3  4 
 1 13 41 51 
data$ICU_15nRev = data$ICU_15n
data$ICU_15nRev = ifelse(data$ICU_15n == 1, 4, data$ICU_15nRev)
data$ICU_15nRev = ifelse(data$ICU_15n == 2, 3, data$ICU_15nRev)
data$ICU_15nRev = ifelse(data$ICU_15n == 3, 2, data$ICU_15nRev)
data$ICU_15nRev = ifelse(data$ICU_15n == 4, 1, data$ICU_15nRev)
table(data$ICU_15nRev)

 1  2  3  4 
51 41 13  1 
table(data$ICU_23n)

 1  2  3  4 
 1 16 40 49 
data$ICU_23nRev = data$ICU_23n
data$ICU_23nRev = ifelse(data$ICU_23n == 1, 4, data$ICU_23nRev)
data$ICU_23nRev = ifelse(data$ICU_23n == 2, 3, data$ICU_23nRev)
data$ICU_23nRev = ifelse(data$ICU_23n == 3, 2, data$ICU_23nRev)
data$ICU_23nRev = ifelse(data$ICU_23n == 4, 1, data$ICU_23nRev)
table(data$ICU_23nRev)

 1  2  3  4 
49 40 16  1 
table(data$ICU_16n)

 2  3  4 
11 43 52 
data$ICU_16nRev = data$ICU_16n
data$ICU_16nRev = ifelse(data$ICU_16n == 1, 4, data$ICU_16nRev)
data$ICU_16nRev = ifelse(data$ICU_16n == 2, 3, data$ICU_16nRev)
data$ICU_16nRev = ifelse(data$ICU_16n == 3, 2, data$ICU_16nRev)
data$ICU_16nRev = ifelse(data$ICU_16n == 4, 1, data$ICU_16nRev)
table(data$ICU_16nRev)

 1  2  3 
52 43 11 
table(data$ICU_3n)

 1  2  3  4 
 1  3 29 71 
data$ICU_3nRev = data$ICU_3n
data$ICU_3nRev = ifelse(data$ICU_3n == 1, 4, data$ICU_3nRev)
data$ICU_3nRev = ifelse(data$ICU_3n == 2, 3, data$ICU_3nRev)
data$ICU_3nRev = ifelse(data$ICU_3n == 3, 2, data$ICU_3nRev)
data$ICU_3nRev = ifelse(data$ICU_3n == 4, 1, data$ICU_3nRev)
table(data$ICU_3nRev)

 1  2  3  4 
71 29  3  1 
table(data$ICU_17n)

 2  3  4 
 6 45 55 
data$ICU_17nRev = data$ICU_17n
data$ICU_17nRev = ifelse(data$ICU_17n == 1, 4, data$ICU_17nRev)
data$ICU_17nRev = ifelse(data$ICU_17n == 2, 3, data$ICU_17nRev)
data$ICU_17nRev = ifelse(data$ICU_17n == 3, 2, data$ICU_17nRev)
data$ICU_17nRev = ifelse(data$ICU_17n == 4, 1, data$ICU_17nRev)
table(data$ICU_17nRev)

 1  2  3 
55 45  6 
table(data$ICU_24n)

 1  2  3  4 
 6 25 44 31 
data$ICU_24nRev = data$ICU_24n
data$ICU_24nRev = ifelse(data$ICU_24n == 1, 4, data$ICU_24nRev)
data$ICU_24nRev = ifelse(data$ICU_24n == 2, 3, data$ICU_24nRev)
data$ICU_24nRev = ifelse(data$ICU_24n == 3, 2, data$ICU_24nRev)
data$ICU_24nRev = ifelse(data$ICU_24n == 4, 1, data$ICU_24nRev)
table(data$ICU_24nRev)

 1  2  3  4 
31 44 25  6 
table(data$ICU_13n)

 1  2  3  4 
 7 46 43 10 
data$ICU_13nRev = data$ICU_13n
data$ICU_13nRev = ifelse(data$ICU_13n == 1, 4, data$ICU_13nRev)
data$ICU_13nRev = ifelse(data$ICU_13n == 2, 3, data$ICU_13nRev)
data$ICU_13nRev = ifelse(data$ICU_13n == 3, 2, data$ICU_13nRev)
data$ICU_13nRev = ifelse(data$ICU_13n == 4, 1, data$ICU_13nRev)
table(data$ICU_13nRev)

 1  2  3  4 
10 43 46  7 
table(data$ICU_5n)

 1  2  3  4 
 4 16 42 44 
data$ICU_5nRev = data$ICU_5n
data$ICU_5nRev = ifelse(data$ICU_5n == 1, 4, data$ICU_5nRev)
data$ICU_5nRev = ifelse(data$ICU_5n == 2, 3, data$ICU_5nRev)
data$ICU_5nRev = ifelse(data$ICU_5n == 3, 2, data$ICU_5nRev)
data$ICU_5nRev = ifelse(data$ICU_5n == 4, 1, data$ICU_5nRev)
table(data$ICU_5nRev)

 1  2  3  4 
44 42 16  4 
# Unemotional 

table(data$ICU_1n)

 1  2  3  4 
24 48 23 11 
data$ICU_1nRev = data$ICU_1n
data$ICU_1nRev = ifelse(data$ICU_1n == 1, 4, data$ICU_1nRev)
data$ICU_1nRev = ifelse(data$ICU_1n == 2, 3, data$ICU_1nRev)
data$ICU_1nRev = ifelse(data$ICU_1n == 3, 2, data$ICU_1nRev)
data$ICU_1nRev = ifelse(data$ICU_1n == 4, 1, data$ICU_1nRev)
table(data$ICU_1nRev)

 1  2  3  4 
11 23 48 24 
table(data$ICU_19n)

 1  2  3  4 
29 35 26 16 
data$ICU_19nRev = data$ICU_19n
data$ICU_19nRev = ifelse(data$ICU_19n == 1, 4, data$ICU_19nRev)
data$ICU_19nRev = ifelse(data$ICU_19n == 2, 3, data$ICU_19nRev)
data$ICU_19nRev = ifelse(data$ICU_19n == 3, 2, data$ICU_19nRev)
data$ICU_19nRev = ifelse(data$ICU_19n == 4, 1, data$ICU_19nRev)
table(data$ICU_19nRev)

 1  2  3  4 
16 26 35 29 
table(data$ICU_14n)

 1  2  3  4 
26 47 24  9 
data$ICU_14nRev = data$ICU_14n
data$ICU_14nRev = ifelse(data$ICU_14n == 1, 4, data$ICU_14nRev)
data$ICU_14nRev = ifelse(data$ICU_14n == 2, 3, data$ICU_14nRev)
data$ICU_14nRev = ifelse(data$ICU_14n == 3, 2, data$ICU_14nRev)
data$ICU_14nRev = ifelse(data$ICU_14n == 4, 1, data$ICU_14nRev)
table(data$ICU_14nRev)

 1  2  3  4 
 9 24 47 26 

SSS

(1, 29, 32, 36, 5, 8, 24, 34, 39, 3, 16, 17, 28, 6, 9, 14, 18, 22)

Recoding was done by creating a “false object” or a place holder since it is a binary scale. Example below.

Diagram of recode

\[ A (OriginalValue) -> C(Placeholder) \] \[ B(OrginalValue) -> A(ReversedValue) \]

\[ C(Placeholder) -> B(ReverseValue) \]

Code Reference

# Disinhibition 


table(data$ZSSS_1n)

 0  1 
22 84 
data$ZSSS_1nRevFalse <- data$ZSSS_1n
data$ZSSS_1nRevFalse = ifelse(data$ZSSS_1n == 0, 2, data$ZSSS_1nRevFalse)
data$ZSSS_1nRevFalse = ifelse(data$ZSSS_1n == 1, 0, data$ZSSS_1nRevFalse)
table(data$ZSSS_1nRevFalse)

 0  2 
84 22 
data$ZSSS_1nRev <- data$ZSSS_1nRevFalse
data$ZSSS_1nRev <- ifelse(data$ZSSS_1nRevFalse == 2, 1, data$ZSSS_1nRev)
table(data$ZSSS_1nRev)

 0  1 
84 22 
table(data$ZSSS_29n)

 0  1 
19 86 
data$ZSSS_29nRevFalse <- data$ZSSS_29n
data$ZSSS_29nRevFalse = ifelse(data$ZSSS_29n == 0, 2, data$ZSSS_29nRevFalse)
data$ZSSS_29nRevFalse = ifelse(data$ZSSS_29n == 1, 0, data$ZSSS_29nRevFalse)
table(data$ZSSS_29nRevFalse)

 0  2 
86 19 
data$ZSSS_29nRev <- data$ZSSS_29nRevFalse
data$ZSSS_29nRev <- ifelse(data$ZSSS_29nRevFalse == 2, 1, data$ZSSS_29nRev)
table(data$ZSSS_29nRev)

 0  1 
86 19 
table(data$ZSSS_32n)

 0  1 
65 41 
data$ZSSS_32nRevFalse <- data$ZSSS_32n
data$ZSSS_32nRevFalse = ifelse(data$ZSSS_32n == 0, 2, data$ZSSS_32nRevFalse)
data$ZSSS_32nRevFalse = ifelse(data$ZSSS_32n == 1, 0, data$ZSSS_32nRevFalse)
table(data$ZSSS_32nRevFalse)

 0  2 
41 65 
data$ZSSS_32nRev <- data$ZSSS_32nRevFalse
data$ZSSS_32nRev <- ifelse(data$ZSSS_32nRevFalse == 2, 1, data$ZSSS_32nRev)
table(data$ZSSS_32nRev)

 0  1 
41 65 
table(data$ZSSS_36n)

 0  1 
41 63 
data$ZSSS_36nRevFalse <- data$ZSSS_36n
data$ZSSS_36nRevFalse = ifelse(data$ZSSS_36n == 0, 2, data$ZSSS_36nRevFalse)
data$ZSSS_36nRevFalse = ifelse(data$ZSSS_36n == 1, 0, data$ZSSS_36nRevFalse)
table(data$ZSSS_36nRevFalse)

 0  2 
63 41 
data$ZSSS_36nRev <- data$ZSSS_36nRevFalse
data$ZSSS_36nRev <- ifelse(data$ZSSS_36nRevFalse == 2, 1, data$ZSSS_36nRev)
table(data$ZSSS_36nRev)

 0  1 
63 41 
# Boredom 



table(data$ZSSS_5n)

 0  1 
12 94 
data$ZSSS_5nRevFalse <- data$ZSSS_5n
data$ZSSS_5nRevFalse = ifelse(data$ZSSS_5n == 0, 2, data$ZSSS_5nRevFalse)
data$ZSSS_5nRevFalse = ifelse(data$ZSSS_5n == 1, 0, data$ZSSS_5nRevFalse)
table(data$ZSSS_5nRevFalse)

 0  2 
94 12 
data$ZSSS_5nRev <- data$ZSSS_5nRevFalse
data$ZSSS_5nRev <- ifelse(data$ZSSS_5nRevFalse == 2, 1, data$ZSSS_5nRev)
table(data$ZSSS_5nRev)

 0  1 
94 12 
table(data$ZSSS_8n)

 0  1 
31 75 
data$ZSSS_8nRevFalse <- data$ZSSS_8n
data$ZSSS_8nRevFalse = ifelse(data$ZSSS_8n == 0, 2, data$ZSSS_8nRevFalse)
data$ZSSS_8nRevFalse = ifelse(data$ZSSS_8n == 1, 0, data$ZSSS_8nRevFalse)
table(data$ZSSS_8nRevFalse)

 0  2 
75 31 
data$ZSSS_8nRev <- data$ZSSS_8nRevFalse
data$ZSSS_8nRev <- ifelse(data$ZSSS_8nRevFalse == 2, 1, data$ZSSS_8nRev)
table(data$ZSSS_8nRev)

 0  1 
75 31 
table(data$ZSSS_24n)

 0  1 
24 82 
data$ZSSS_24nRevFalse <- data$ZSSS_24n
data$ZSSS_24nRevFalse = ifelse(data$ZSSS_24n == 0, 2, data$ZSSS_24nRevFalse)
data$ZSSS_24nRevFalse = ifelse(data$ZSSS_24n == 1, 0, data$ZSSS_24nRevFalse)
table(data$ZSSS_24nRevFalse)

 0  2 
82 24 
data$ZSSS_24nRev <- data$ZSSS_24nRevFalse
data$ZSSS_24nRev <- ifelse(data$ZSSS_24nRevFalse == 2, 1, data$ZSSS_24nRev)
table(data$ZSSS_24nRev)

 0  1 
82 24 
table(data$ZSSS_34n)

 0  1 
33 73 
data$ZSSS_34nRevFalse <- data$ZSSS_34n
data$ZSSS_34nRevFalse = ifelse(data$ZSSS_34n == 0, 2, data$ZSSS_34nRevFalse)
data$ZSSS_34nRevFalse = ifelse(data$ZSSS_34n == 1, 0, data$ZSSS_34nRevFalse)
table(data$ZSSS_34nRevFalse)

 0  2 
73 33 
data$ZSSS_34nRev <- data$ZSSS_34nRevFalse
data$ZSSS_34nRev <- ifelse(data$ZSSS_34nRevFalse == 2, 1, data$ZSSS_34nRev)
table(data$ZSSS_34nRev)

 0  1 
73 33 
table(data$ZSSS_39n)

 0  1 
26 80 
data$ZSSS_39nRevFalse <- data$ZSSS_39n
data$ZSSS_39nRevFalse = ifelse(data$ZSSS_39n == 0, 2, data$ZSSS_39nRevFalse)
data$ZSSS_39nRevFalse = ifelse(data$ZSSS_39n == 1, 0, data$ZSSS_39nRevFalse)
table(data$ZSSS_39nRevFalse)

 0  2 
80 26 
data$ZSSS_39nRev <- data$ZSSS_39nRevFalse
data$ZSSS_39nRev <- ifelse(data$ZSSS_39nRevFalse == 2, 1, data$ZSSS_39nRev)
table(data$ZSSS_39nRev)

 0  1 
80 26 
# Thrill 


table(data$ZSSS_3n)

 0  1 
61 45 
data$ZSSS_3nRevFalse <- data$ZSSS_3n
data$ZSSS_3nRevFalse = ifelse(data$ZSSS_3n == 0, 2, data$ZSSS_3nRevFalse)
data$ZSSS_3nRevFalse = ifelse(data$ZSSS_3n == 1, 0, data$ZSSS_3nRevFalse)
table(data$ZSSS_3nRevFalse)

 0  2 
45 61 
data$ZSSS_3nRev <- data$ZSSS_3nRevFalse
data$ZSSS_3nRev <- ifelse(data$ZSSS_3nRevFalse == 2, 1, data$ZSSS_3nRev)
table(data$ZSSS_3nRev)

 0  1 
45 61 
table(data$ZSSS_16n)

 0  1 
67 39 
data$ZSSS_16nRevFalse <- data$ZSSS_16n
data$ZSSS_16nRevFalse = ifelse(data$ZSSS_16n == 0, 2, data$ZSSS_16nRevFalse)
data$ZSSS_16nRevFalse = ifelse(data$ZSSS_16n == 1, 0, data$ZSSS_16nRevFalse)
table(data$ZSSS_16nRevFalse)

 0  2 
39 67 
data$ZSSS_16nRev <- data$ZSSS_16nRevFalse
data$ZSSS_16nRev <- ifelse(data$ZSSS_16nRevFalse == 2, 1, data$ZSSS_16nRev)
table(data$ZSSS_16nRev)

 0  1 
39 67 
table(data$ZSSS_17n)

 0  1 
80 26 
data$ZSSS_17nRevFalse <- data$ZSSS_17n
data$ZSSS_17nRevFalse = ifelse(data$ZSSS_17n == 0, 2, data$ZSSS_17nRevFalse)
data$ZSSS_17nRevFalse = ifelse(data$ZSSS_17n == 1, 0, data$ZSSS_17nRevFalse)
table(data$ZSSS_17nRevFalse)

 0  2 
26 80 
data$ZSSS_17nRev <- data$ZSSS_17nRevFalse
data$ZSSS_17nRev <- ifelse(data$ZSSS_17nRevFalse == 2, 1, data$ZSSS_17nRev)
table(data$ZSSS_17nRev)

 0  1 
26 80 
table(data$ZSSS_23n)

 0  1 
70 36 
data$ZSSS_23nRevFalse <- data$ZSSS_23n
data$ZSSS_23nRevFalse = ifelse(data$ZSSS_23n == 0, 2, data$ZSSS_23nRevFalse)
data$ZSSS_23nRevFalse = ifelse(data$ZSSS_23n == 1, 0, data$ZSSS_23nRevFalse)
table(data$ZSSS_23nRevFalse)

 0  2 
36 70 
data$ZSSS_23nRev <- data$ZSSS_23nRevFalse
data$ZSSS_23nRev <- ifelse(data$ZSSS_23nRevFalse == 2, 1, data$ZSSS_23nRev)
table(data$ZSSS_23nRev)

 0  1 
36 70 
table(data$ZSSS_28n)

 0  1 
45 60 
data$ZSSS_28nRevFalse <- data$ZSSS_28n
data$ZSSS_28nRevFalse = ifelse(data$ZSSS_28n == 0, 2, data$ZSSS_28nRevFalse)
data$ZSSS_28nRevFalse = ifelse(data$ZSSS_28n == 1, 0, data$ZSSS_28nRevFalse)
table(data$ZSSS_28nRevFalse)

 0  2 
60 45 
data$ZSSS_28nRev <- data$ZSSS_28nRevFalse
data$ZSSS_28nRev <- ifelse(data$ZSSS_28nRevFalse == 2, 1, data$ZSSS_28nRev)
table(data$ZSSS_28nRev)

 0  1 
60 45 
# Exp 


table(data$ZSSS_6n)

 0  1 
61 45 
data$ZSSS_6nRevFalse <- data$ZSSS_6n
data$ZSSS_6nRevFalse = ifelse(data$ZSSS_6n == 0, 2, data$ZSSS_6nRevFalse)
data$ZSSS_6nRevFalse = ifelse(data$ZSSS_6n == 1, 0, data$ZSSS_6nRevFalse)
table(data$ZSSS_6nRevFalse)

 0  2 
45 61 
data$ZSSS_6nRev <- data$ZSSS_6nRevFalse
data$ZSSS_6nRev <- ifelse(data$ZSSS_6nRevFalse == 2, 1, data$ZSSS_6nRev)
table(data$ZSSS_6nRev)

 0  1 
45 61 
table(data$ZSSS_9n)

 0  1 
61 45 
data$ZSSS_9nRevFalse <- data$ZSSS_9n
data$ZSSS_9nRevFalse = ifelse(data$ZSSS_9n == 0, 2, data$ZSSS_9nRevFalse)
data$ZSSS_9nRevFalse = ifelse(data$ZSSS_9n == 1, 0, data$ZSSS_9nRevFalse)
table(data$ZSSS_9nRevFalse)

 0  2 
45 61 
data$ZSSS_9nRev <- data$ZSSS_9nRevFalse
data$ZSSS_9nRev <- ifelse(data$ZSSS_9nRevFalse == 2, 1, data$ZSSS_9nRev)
table(data$ZSSS_9nRev)

 0  1 
45 61 
table(data$ZSSS_14n)

 0  1 
59 47 
data$ZSSS_14nRevFalse <- data$ZSSS_14n
data$ZSSS_14nRevFalse = ifelse(data$ZSSS_14n == 0, 2, data$ZSSS_14nRevFalse)
data$ZSSS_14nRevFalse = ifelse(data$ZSSS_14n == 1, 0, data$ZSSS_14nRevFalse)
table(data$ZSSS_14nRevFalse)

 0  2 
47 59 
data$ZSSS_14nRev <- data$ZSSS_14nRevFalse
data$ZSSS_14nRev <- ifelse(data$ZSSS_14nRevFalse == 2, 1, data$ZSSS_14nRev)
table(data$ZSSS_14nRev)

 0  1 
47 59 
table(data$ZSSS_18n)

 0  1 
51 55 
data$ZSSS_18nRevFalse <- data$ZSSS_18n
data$ZSSS_18nRevFalse = ifelse(data$ZSSS_18n == 0, 2, data$ZSSS_18nRevFalse)
data$ZSSS_18nRevFalse = ifelse(data$ZSSS_18n == 1, 0, data$ZSSS_18nRevFalse)
table(data$ZSSS_18nRevFalse)

 0  2 
55 51 
data$ZSSS_18nRev <- data$ZSSS_18nRevFalse
data$ZSSS_18nRev <- ifelse(data$ZSSS_18nRevFalse == 2, 1, data$ZSSS_18nRev)
table(data$ZSSS_18nRev)

 0  1 
55 51 
table(data$ZSSS_22n)

 0  1 
91 14 
data$ZSSS_22nRevFalse <- data$ZSSS_22n
data$ZSSS_22nRevFalse = ifelse(data$ZSSS_22n == 0, 2, data$ZSSS_22nRevFalse)
data$ZSSS_22nRevFalse = ifelse(data$ZSSS_22n == 1, 0, data$ZSSS_22nRevFalse)
table(data$ZSSS_22nRevFalse)

 0  2 
14 91 
data$ZSSS_22nRev <- data$ZSSS_22nRevFalse
data$ZSSS_22nRev <- ifelse(data$ZSSS_22nRevFalse == 2, 1, data$ZSSS_22nRev)
table(data$ZSSS_22nRev)

 0  1 
14 91 

Scales

SRP

# SRP Tot

data$SRPTotalScore <- (data$SRP_01n + data$SRP_02n + data$SRP_03n + data$SRP_04n +  data$SRP5nRev + data$SRP6nRev + data$SRP_07n + 
                  data$SRP_08n + data$SRP_09n + data$SRP_10n + data$SRP11nRev + data$SRP_12n + data$SRP_13n + data$SRP14nRev +
                  data$SRP_15n + data$SRP16nRev + data$SRP_17n + data$SRP18nRev + data$SRP19nRev + data$SRP_20n + data$SRP21nRev +
                  data$SRP22nRev + data$SRP23nRev + data$SRP24nRev + data$SRP25nRev + data$SRP26nRev + data$SRP_27n + data$SRP_28n + 
                  data$SRP_29n + data$SRP_30n + data$SRP31nRev + data$SRP_32n + data$SRP_33n + data$SRP34nRev +  data$SRP_35n + 
                  data$SRP36nRev +  data$SRP_37n + data$SRP38nRev + data$SRP_39n + data$SRP_40n + data$SRP_41n + data$SRP_42n + 
                  data$SRP_43n + data$SRP44nRev + data$SRP_45n + data$SRP46nRev + data$SRP47nRev + data$SRP_48n + data$SRP_49n + 
                  data$SRP_50n + data$SRP_51n + data$SRP_52n + data$SRP_53n + data$SRP_54n + data$SRP_55n +  data$SRP_56n +
                  data$SRP_57n + data$SRP_58n + data$SRP_59n + data$SRP_60n + data$SRP61nRev + data$SRP_62n + data$SRP_63n + data$SRP_64n)



#SRP IPM 

data$SRPIPMTotal <- (data$SRP_03n + data$SRP_08n + data$SRP_13n + data$SRP16nRev + data$SRP_20n + data$SRP24nRev + data$SRP_27n + data$SRP31nRev + 
                  data$SRP_35n + data$SRP38nRev + data$SRP_41n + data$SRP_45n + data$SRP_50n + data$SRP_54n + data$SRP_58n + data$SRP61nRev)



# SRP Callous 

data$SRPCATotal <- (data$SRP_02n + data$SRP_07n + data$SRP11nRev + data$SRP_15n + data$SRP19nRev + data$SRP23nRev + data$SRP26nRev + data$SRP_30n + data$SRP_33n +  data$SRP_37n + data$SRP_40n + data$SRP44nRev + data$SRP_48n + data$SRP_53n + data$SRP_56n + data$SRP_60n)





#SRP lifestyle 

data$SRPELSTotal <- (data$SRP_01n + data$SRP_04n + data$SRP_09n + data$SRP14nRev + data$SRP_17n + data$SRP22nRev + data$SRP25nRev + data$SRP_28n + data$SRP_32n + data$SRP36nRev +  data$SRP_39n + data$SRP_42n + data$SRP47nRev + data$SRP_51n +data$SRP_55n + data$SRP_59n)


# SRP Antisocial 


data$SRPASBTotal <-  (data$SRP5nRev + data$SRP6nRev + data$SRP_10n + data$SRP_12n + data$SRP18nRev + data$SRP21nRev + data$SRP_29n + data$SRP34nRev + data$SRP_43n + data$SRP46nRev + data$SRP_49n + data$SRP_52n + data$SRP_57n + data$SRP_62n + data$SRP_63n + data$SRP_64n)

ICU

# ICU total 

data$ICUTotScore <- (data$ICU_1nRev + data$ICU_2n + data$ICU_3nRev + data$ICU_4n + data$ICU_5nRev + data$ICU_6n + 
                  data$ICU_7n + data$ICU_8nRev + data$ICU_9n + data$ICU_10n + data$ICU_11n + data$ICU_12n + data$ICU_13nRev +
                  data$ICU_14nRev + data$ICU_15nRev + data$ICU_16nRev + data$ICU_17nRev + data$ICU_18n + data$ICU_19nRev +
                  data$ICU_20n + data$ICU_21n + data$ICU_22n + data$ICU_23nRev + data$ICU_24nRev)


# ICU Cal 

data$ICUCalTotalScore <- (data$ICU_4n + data$ICU_8nRev + data$ICU_9n + data$ICU_18n + data$ICU_11n +  data$ICU_21n + data$ICU_7n + data$ICU_20n +
                  data$ICU_2n + data$ICU_12n + data$ICU_10n)





# ICU Uncare

data$ICUUncareTotalScore <- (data$ICU_15nRev + data$ICU_23nRev + data$ICU_16nRev + data$ICU_3nRev + data$ICU_17nRev + data$ICU_24nRev +
                     data$ICU_13nRev + data$ICU_5nRev)




# ICU  Unemo 


data$ICUUnemoTotal <- (data$ICU_1nRev + data$ICU_19nRev + data$ICU_6n + data$ICU_22n + data$ICU_14nRev)

LSRP

# Total

data$LevTotalScore <- (data$Lev_01n + data$Lev_02n + data$Lev_03nRev + data$Lev_04n + data$Lev_05n + data$Lev_06n + data$Lev_07nRev + data$Lev_08n +  data$Lev_09n + data$Lev_10nRev + data$Lev_11n + data$Lev_12n + data$Lev_13nRev + data$Lev_16n + data$Lev_17n + data$Lev_18n + data$Lev_19n + data$Lev_20n + data$Lev_21nRev + data$Lev_22n + data$Lev_23n + data$Lev_24n + data$Lev_25n + data$Lev_26nRev)



# Primary 


data$LevPrimTotalScore <- (data$Lev_02n + data$Lev_04n + data$Lev_07nRev + data$Lev_09n + data$Lev_11n + data$Lev_12n + data$Lev_13nRev +
                   data$Lev_17n + data$Lev_19n +  data$Lev_21nRev + data$Lev_22n + data$Lev_23n + data$Lev_24n + data$Lev_25n + data$Lev_26nRev)






# Seconnday 

data$LevSecTotalScore <- (data$Lev_01n + data$Lev_03nRev + data$Lev_05n + data$Lev_06n + data$Lev_08n + data$Lev_10nRev + data$Lev_16n + data$Lev_18n + data$Lev_20n)

ZSSS

# Total 

data$SSSTotalScore <-  (data$ZSSS_1nRev + data$ZSSS_2n + data$ZSSS_3nRev + data$ZSSS_4n + data$ZSSS_5nRev + data$ZSSS_6nRev + data$ZSSS_7n + data$ZSSS_8nRev + data$ZSSS_9nRev + data$ZSSS_10n + data$ZSSS_11n + data$ZSSS_12n + data$ZSSS_13n + data$ZSSS_14nRev + data$ZSSS_15n + data$ZSSS_16nRev + data$ZSSS_17nRev + data$ZSSS_18nRev + data$ZSSS_19n + data$ZSSS_20n +  data$ZSSS_21n + data$ZSSS_22nRev + data$ZSSS_23nRev + data$ZSSS_24nRev + data$ZSSS_25n + data$ZSSS_26n + data$ZSSS_27n + data$ZSSS_28nRev + data$ZSSS_29nRev + data$ZSSS_30n + data$ZSSS_31n + data$ZSSS_32nRev + data$ZSSS_33n + data$ZSSS_34nRev + data$ZSSS_35n + data$ZSSS_36nRev + data$ZSSS_37n + data$ZSSS_38n + data$ZSSS_39nRev + data$ZSSS_40n)






# Disinhibited 

data$SSSDISTotal <- (data$ZSSS_12n + data$ZSSS_13n + data$ZSSS_25n + data$ZSSS_30n + data$ZSSS_33n + data$ZSSS_35n +
                  data$ZSSS_1nRev + data$ZSSS_29nRev + data$ZSSS_32nRev + data$ZSSS_36nRev)




# Boredom 

data$SSSBorTotal <- (data$ZSSS_2n + data$ZSSS_7n + data$ZSSS_15n + data$ZSSS_27n + data$ZSSS_31n + data$ZSSS_5nRev + data$ZSSS_8nRev + data$ZSSS_24nRev +
                  data$ZSSS_34nRev + data$ZSSS_39nRev)




# Thrill 
data$SSSThrilTotal <- (data$ZSSS_11n + data$ZSSS_20n + data$ZSSS_21n + data$ZSSS_38n + data$ZSSS_40n + data$ZSSS_3nRev +
                    data$ZSSS_16nRev + data$ZSSS_17nRev + data$ZSSS_23nRev + data$ZSSS_28nRev)




# Exp 

data$SSSExpTotal <- (data$ZSSS_4n + data$ZSSS_10n + data$ZSSS_19n + data$ZSSS_26n + data$ZSSS_37n + data$ZSSS_6nRev +
                  data$ZSSS_9nRev + data$ZSSS_14nRev + data$ZSSS_18nRev + data$ZSSS_22nRev) 

Autonomic Measures

We excluded zero values from the analysis during the first 11 seconds and the final 30 seconds, as they likely resulted from slight hand movements by the participant during the phases.

Resting Heart Rate

data$HRbaseline <- (data$HRT_00_11 + data$HRT_00_12 +  data$HRT_00_13 +  data$HRT_00_14 + data$HRT_00_15 + data$HRT_00_16 +  data$HRT_00_17 +  data$HRT_00_18 + data$HRT_00_19 + data$HRT_00_20 +  data$HRT_00_21 +  data$HRT_00_22 + data$HRT_00_23 + data$HRT_00_24 +  data$HRT_00_25 + data$HRT_00_26 +  data$HRT_00_27 + data$HRT_00_28 +  data$HRT_00_29 +  data$HRT_00_30 +  data$HRT_00_31 + data$HRT_00_32 +  data$HRT_00_33 +  data$HRT_00_34 + data$HRT_00_35 + data$HRT_00_36 + data$HRT_00_37 +  data$HRT_00_38 +  data$HRT_00_39 + data$HRT_00_40 +  data$HRT_00_41 +  data$HRT_00_42 + data$HRT_00_43 + data$HRT_00_44 + data$HRT_00_45 +  data$HRT_00_46 +  data$HRT_00_47 +  data$HRT_00_48 + data$HRT_00_49 + data$HRT_00_50 +  data$HRT_00_51 +  data$HRT_00_52 +  data$HRT_00_53 + data$HRT_00_54 + data$HRT_00_55 + data$HRT_00_56 +  data$HRT_00_57    +  data$HRT_00_58 + data$HRT_00_59 +  data$HRT_01_00 +  data$HRT_01_01 +  data$HRT_01_02 + data$HRT_01_03 + data$HRT_01_04 + data$HRT_01_05 +  data$HRT_01_06 +  data$HRT_01_07 + data$HRT_01_08 +  data$HRT_01_09 +  data$HRT_01_10 +  data$HRT_01_11 + data$HRT_01_12 + data$HRT_01_13 + data$HRT_01_14 +  data$HRT_01_15 +  data$HRT_01_16 +  data$HRT_01_17 +  data$HRT_01_18 + data$HRT_01_19 +  data$HRT_01_20 +  data$HRT_01_21 +  data$HRT_01_22 + data$HRT_01_23 +  data$HRT_01_24 + data$HRT_01_25 +  data$HRT_01_26 +  data$HRT_01_27 + data$HRT_01_28 +  data$HRT_01_29 +  data$HRT_01_30 +  data$HRT_01_31 + data$HRT_01_32 +  data$HRT_01_33 + data$HRT_01_34 +  data$HRT_01_35 +  data$HRT_01_36 + data$HRT_01_37 +  data$HRT_01_38 +  data$HRT_01_39 +  data$HRT_01_40 +  data$HRT_01_41 + data$HRT_01_42 + data$HRT_01_43 + data$HRT_01_44 +  data$HRT_01_45 + data$HRT_01_46 + data$HRT_01_47 +  data$HRT_01_48 +  data$HRT_01_49 +  data$HRT_01_50 +  data$HRT_01_51 + data$HRT_01_52 + data$HRT_01_53 + data$HRT_01_54 +  data$HRT_01_55 +  data$HRT_01_56 +  data$HRT_01_57 + data$HRT_01_58 +  data$HRT_01_59 +  data$HRT_02_00 +  data$HRT_02_01 + data$HRT_02_02 + data$HRT_02_03 +  data$HRT_02_04 +  data$HRT_02_05 +  data$HRT_02_06 +  data$HRT_02_07 + data$HRT_02_08 + data$HRT_02_09 +  data$HRT_02_10 +  data$HRT_02_11 +  data$HRT_02_12 + data$HRT_02_13 +  data$HRT_02_14 +  data$HRT_02_15 + data$HRT_02_16 +  data$HRT_02_17 + data$HRT_02_18 +  data$HRT_02_19 + data$HRT_02_20 +  data$HRT_02_21 +  data$HRT_02_22 + data$HRT_02_23 +  data$HRT_02_24 + data$HRT_02_25 +  data$HRT_02_26 +  data$HRT_02_27 +  data$HRT_02_28 + data$HRT_02_29)/140

Resting Skin Conductance

data$SCbaseline <- (data$SCT_00_11 + data$SCT_00_12 + data$SCT_00_13 + data$SCT_00_14 + data$SCT_00_15 + data$SCT_00_16 + data$SCT_00_17 + data$SCT_00_18 + data$SCT_00_19 + data$SCT_00_20 + data$SCT_00_21 + data$SCT_00_22 + data$SCT_00_23 + data$SCT_00_24 + data$SCT_00_25 + data$SCT_00_26 + data$SCT_00_27 + data$SCT_00_28 + data$SCT_00_29 + data$SCT_00_30 + data$SCT_00_31 + data$SCT_00_32 + data$SCT_00_33 + data$SCT_00_34 + data$SCT_00_35 + data$SCT_00_36 + data$SCT_00_37 + data$SCT_00_38 + data$SCT_00_39 + data$SCT_00_40 + data$SCT_00_41 + data$SCT_00_42 + data$SCT_00_43 + data$SCT_00_44 + data$SCT_00_45 + data$SCT_00_46 + data$SCT_00_47 + data$SCT_00_48 + data$SCT_00_49 + data$SCT_00_50 + data$SCT_00_51 + data$SCT_00_52 + data$SCT_00_53 + data$SCT_00_54 + data$SCT_00_55 + data$SCT_00_56 + data$SCT_00_57    + data$SCT_00_58 + data$SCT_00_59 + data$SCT_01_00 + data$SCT_01_01 + data$SCT_01_02 + data$SCT_01_03 + data$SCT_01_04 + data$SCT_01_05 + data$SCT_01_06 + data$SCT_01_07 + data$SCT_01_08 + data$SCT_01_09 + data$SCT_01_10 + data$SCT_01_11 + data$SCT_01_12 + data$SCT_01_13 + data$SCT_01_14 + data$SCT_01_15 + data$SCT_01_16 + data$SCT_01_17 + data$SCT_01_18 + data$SCT_01_19 + data$SCT_01_20 + data$SCT_01_21 + data$SCT_01_22 + data$SCT_01_23 + data$SCT_01_24 + data$SCT_01_25 + data$SCT_01_26 + data$SCT_01_27 + data$SCT_01_28 + data$SCT_01_29 + data$SCT_01_30 + data$SCT_01_31 + data$SCT_01_32 + data$SCT_01_33 + data$SCT_01_34 + data$SCT_01_35 + data$SCT_01_36 + data$SCT_01_37 + data$SCT_01_38 + data$SCT_01_39 + data$SCT_01_40 + data$SCT_01_41 + data$SCT_01_42 + data$SCT_01_43 + data$SCT_01_44 + data$SCT_01_45 + data$SCT_01_46 + data$SCT_01_47 + data$SCT_01_48 + data$SCT_01_49 + data$SCT_01_50 + data$SCT_01_51 + data$SCT_01_52 + data$SCT_01_53 + data$SCT_01_54 + data$SCT_01_55 + data$SCT_01_56 + data$SCT_01_57 + data$SCT_01_58 + data$SCT_01_59 + data$SCT_02_00 + data$SCT_02_01 + data$SCT_02_02 + data$SCT_02_03 + data$SCT_02_04 + data$SCT_02_05 + data$SCT_02_06 + data$SCT_02_07 + data$SCT_02_08 + data$SCT_02_09 + data$SCT_02_10 + data$SCT_02_11 + data$SCT_02_12 + data$SCT_02_13 + data$SCT_02_14 + data$SCT_02_15 + data$SCT_02_16 + data$SCT_02_17 + data$SCT_02_18 + data$SCT_02_19 + data$SCT_02_20 + data$SCT_02_21 + data$SCT_02_22 + data$SCT_02_23 + data$SCT_02_24 + data$SCT_02_25 + data$SCT_02_26 + data$SCT_02_27 + data$SCT_02_28 + data$SCT_02_29)/140

The formula for the AUC code below can be found here.

AUC Social Stressor

In the HR measures for each stress paradigm, a small number of individuals had zero values in random columns (typically 1-4 columns or seconds). These zeros likely resulted from minor, random hand movements during the task. While the data appeared to be missing completely at random (MCAR), which would justify excluding these cases, we chose to retain them to maximize sample size. Zero values were replaced by the mean of the values from the second before and the second after the zero.

HR Combined

data$SSHRCombAUCg <- (data$HrStr_00_01 + data$HrStr_00_00)/2 + (data$HrStr_00_02 + data$HrStr_00_01)/2 + (data$HrStr_00_03 + data$HrStr_00_02)/2 + (data$HrStr_00_04 + data$HrStr_00_03)/2 +
  (data$HrStr_00_05 + data$HrStr_00_04)/2 + (data$HrStr_00_06 + data$HrStr_00_05)/2 + (data$HrStr_00_07 + data$HrStr_00_06)/2 + (data$HrStr_00_08 + data$HrStr_00_07)/2 + (data$HrStr_00_09 + data$HrStr_00_08)/2 + 
  (data$HrStr_00_10 + data$HrStr_00_09)/2 + (data$HrStr_00_11 + data$HrStr_00_10)/2 + (data$HrStr_00_12 + data$HrStr_00_11)/2 + (data$HrStr_00_13 + data$HrStr_00_12)/2 + (data$HrStr_00_14 + data$HrStr_00_13)/2 +
  (data$HrStr_00_15 + data$HrStr_00_14)/2 + (data$HrStr_00_16 + data$HrStr_00_15)/2 + (data$HrStr_00_17 + data$HrStr_00_16)/2 + (data$HrStr_00_18 + data$HrStr_00_17)/2 + (data$HrStr_00_19 + data$HrStr_00_18)/2 +
  (data$HrStr_00_20 + data$HrStr_00_19)/2 + (data$HrStr_00_21 + data$HrStr_00_20)/2 + (data$HrStr_00_22 + data$HrStr_00_21)/2 + (data$HrStr_00_23 + data$HrStr_00_22)/2 + (data$HrStr_00_24 + data$HrStr_00_23)/2 +
  (data$HrStr_00_25 + data$HrStr_00_24)/2 + (data$HrStr_00_26 + data$HrStr_00_25)/2 + (data$HrStr_00_27 + data$HrStr_00_26)/2 + (data$HrStr_00_28 + data$HrStr_00_27)/2 + (data$HrStr_00_29 + data$HrStr_00_28)/2 +
  (data$HrStr_00_30 + data$HrStr_00_29)/2 + (data$HrStr_00_31 + data$HrStr_00_30)/2 + (data$HrStr_00_32 + data$HrStr_00_31)/2 + (data$HrStr_00_33 + data$HrStr_00_32)/2 + (data$HrStr_00_34 + data$HrStr_00_33)/2 +
  (data$HrStr_00_35 + data$HrStr_00_34)/2 + (data$HrStr_00_36 + data$HrStr_00_35)/2 + (data$HrStr_00_37 + data$HrStr_00_36)/2 + (data$HrStr_00_38 + data$HrStr_00_37)/2 + (data$HrStr_00_39 + data$HrStr_00_38)/2 +
  (data$HrStr_00_40 + data$HrStr_00_39)/2 + (data$HrStr_00_41 + data$HrStr_00_40)/2 + (data$HrStr_00_42 + data$HrStr_00_41)/2 + (data$HrStr_00_43 + data$HrStr_00_42)/2 + (data$HrStr_00_44 + data$HrStr_00_43)/2 +
  (data$HrStr_00_45 + data$HrStr_00_44)/2 + (data$HrStr_00_46 + data$HrStr_00_45)/2 + (data$HrStr_00_47 + data$HrStr_00_46)/2 + (data$HrStr_00_48 + data$HrStr_00_47)/2 + (data$HrStr_00_49 + data$HrStr_00_48)/2 +
  (data$HrStr_00_50 + data$HrStr_00_49)/2 + (data$HrStr_00_51 + data$HrStr_00_50)/2 + (data$HrStr_00_52 + data$HrStr_00_51)/2 + (data$HrStr_00_53 + data$HrStr_00_52)/2 + (data$HrStr_00_54 + data$HrStr_00_53)/2 +
  (data$HrStr_00_55 + data$HrStr_00_54)/2 + (data$HrStr_00_56 + data$HrStr_00_55)/2 + (data$HrStr_00_57 + data$HrStr_00_56)/2 + (data$HrStr_00_58 + data$HrStr_00_57)/2 + (data$HrStr_00_59 + data$HrStr_00_58)/2 +
  (data$HrStr_01_00 + data$HrStr_00_59)/2 +
  (data$HrStr_01_01 + data$HrStr_01_00)/2 + (data$HrStr_01_02 + data$HrStr_01_01)/2 + (data$HrStr_01_03 + data$HrStr_01_02)/2 + (data$HrStr_01_04 + data$HrStr_01_03)/2 +
  (data$HrStr_01_05 + data$HrStr_01_04)/2 + (data$HrStr_01_06 + data$HrStr_01_05)/2 + (data$HrStr_01_07 + data$HrStr_01_06)/2 + (data$HrStr_01_08 + data$HrStr_01_07)/2 + (data$HrStr_01_09 + data$HrStr_01_08)/2 + 
  (data$HrStr_01_10 + data$HrStr_01_09)/2 + (data$HrStr_01_11 + data$HrStr_01_10)/2 + (data$HrStr_01_12 + data$HrStr_01_11)/2 + (data$HrStr_01_13 + data$HrStr_01_12)/2 + (data$HrStr_01_14 + data$HrStr_01_13)/2 +
  (data$HrStr_01_15 + data$HrStr_01_14)/2 + (data$HrStr_01_16 + data$HrStr_01_15)/2 + (data$HrStr_01_17 + data$HrStr_01_16)/2 + (data$HrStr_01_18 + data$HrStr_01_17)/2 + (data$HrStr_01_19 + data$HrStr_01_18)/2 +
  (data$HrStr_01_20 + data$HrStr_01_19)/2 + (data$HrStr_01_21 + data$HrStr_01_20)/2 + (data$HrStr_01_22 + data$HrStr_01_21)/2 + (data$HrStr_01_23 + data$HrStr_01_22)/2 + (data$HrStr_01_24 + data$HrStr_01_23)/2 +
  (data$HrStr_01_25 + data$HrStr_01_24)/2 + (data$HrStr_01_26 + data$HrStr_01_25)/2 + (data$HrStr_01_27 + data$HrStr_01_26)/2 + (data$HrStr_01_28 + data$HrStr_01_27)/2 + (data$HrStr_01_29 + data$HrStr_01_28)/2 +
  (data$HrStr_01_30 + data$HrStr_01_29)/2 + (data$HrStr_01_31 + data$HrStr_01_30)/2 + (data$HrStr_01_32 + data$HrStr_01_31)/2 + (data$HrStr_01_33 + data$HrStr_01_32)/2 + (data$HrStr_01_34 + data$HrStr_01_33)/2 +
  (data$HrStr_01_35 + data$HrStr_01_34)/2 + (data$HrStr_01_36 + data$HrStr_01_35)/2 + (data$HrStr_01_37 + data$HrStr_01_36)/2 + (data$HrStr_01_38 + data$HrStr_01_37)/2 + (data$HrStr_01_39 + data$HrStr_01_38)/2 +
  (data$HrStr_01_40 + data$HrStr_01_39)/2 + (data$HrStr_01_41 + data$HrStr_01_40)/2 + (data$HrStr_01_42 + data$HrStr_01_41)/2 + (data$HrStr_01_43 + data$HrStr_01_42)/2 + (data$HrStr_01_44 + data$HrStr_01_43)/2 +
  (data$HrStr_01_45 + data$HrStr_01_44)/2 + (data$HrStr_01_46 + data$HrStr_01_45)/2 + (data$HrStr_01_47 + data$HrStr_01_46)/2 + (data$HrStr_01_48 + data$HrStr_01_47)/2 + (data$HrStr_01_49 + data$HrStr_01_48)/2 +
  (data$HrStr_01_50 + data$HrStr_01_49)/2 + (data$HrStr_01_51 + data$HrStr_01_50)/2 + (data$HrStr_01_52 + data$HrStr_01_51)/2 + (data$HrStr_01_53 + data$HrStr_01_52)/2 + (data$HrStr_01_54 + data$HrStr_01_53)/2 +
  (data$HrStr_01_55 + data$HrStr_01_54)/2 + (data$HrStr_01_56 + data$HrStr_01_55)/2 + (data$HrStr_01_57 + data$HrStr_01_56)/2 + (data$HrStr_01_58 + data$HrStr_01_57)/2 + (data$HrStr_01_59 + data$HrStr_01_58)/2 +
  (data$HrStr_02_00 + data$HrStr_01_59)/2 +
  (data$HrStr_02_01 + data$HrStr_02_00)/2 + (data$HrStr_02_02 + data$HrStr_02_01)/2 + (data$HrStr_02_03 + data$HrStr_02_02)/2 + (data$HrStr_02_04 + data$HrStr_02_03)/2 +
  (data$HrStr_02_05 + data$HrStr_02_04)/2 + (data$HrStr_02_06 + data$HrStr_02_05)/2 + (data$HrStr_02_07 + data$HrStr_02_06)/2 + (data$HrStr_02_08 + data$HrStr_02_07)/2 + (data$HrStr_02_09 + data$HrStr_02_08)/2 + 
  (data$HrStr_02_10 + data$HrStr_02_09)/2 + (data$HrStr_02_11 + data$HrStr_02_10)/2 + (data$HrStr_02_12 + data$HrStr_02_11)/2 + (data$HrStr_02_13 + data$HrStr_02_12)/2 + (data$HrStr_02_14 + data$HrStr_02_13)/2 +
  (data$HrStr_02_15 + data$HrStr_02_14)/2 + (data$HrStr_02_16 + data$HrStr_02_15)/2 + (data$HrStr_02_17 + data$HrStr_02_16)/2 + (data$HrStr_02_18 + data$HrStr_02_17)/2 + (data$HrStr_02_19 + data$HrStr_02_18)/2 +
  (data$HrStr_02_20 + data$HrStr_02_19)/2 + (data$HrStr_02_21 + data$HrStr_02_20)/2 + (data$HrStr_02_22 + data$HrStr_02_21)/2 + (data$HrStr_02_23 + data$HrStr_02_22)/2 + (data$HrStr_02_24 + data$HrStr_02_23)/2 +
  (data$HrStr_02_25 + data$HrStr_02_24)/2 + (data$HrStr_02_26 + data$HrStr_02_25)/2 + (data$HrStr_02_27 + data$HrStr_02_26)/2 + (data$HrStr_02_28 + data$HrStr_02_27)/2 + (data$HrStr_02_29 + data$HrStr_02_28)/2 +
  (data$HrStr_02_30 + data$HrStr_02_29)/2 + (data$HrStr_02_31 + data$HrStr_02_30)/2 + (data$HrStr_02_32 + data$HrStr_02_31)/2 + (data$HrStr_02_33 + data$HrStr_02_32)/2 + (data$HrStr_02_34 + data$HrStr_02_33)/2 +
  (data$HrStr_02_35 + data$HrStr_02_34)/2 + (data$HrStr_02_36 + data$HrStr_02_35)/2 + (data$HrStr_02_37 + data$HrStr_02_36)/2 + (data$HrStr_02_38 + data$HrStr_02_37)/2 + (data$HrStr_02_39 + data$HrStr_02_38)/2 +
  (data$HrStr_02_40 + data$HrStr_02_39)/2 + (data$HrStr_02_41 + data$HrStr_02_40)/2 + (data$HrStr_02_42 + data$HrStr_02_41)/2 + (data$HrStr_02_43 + data$HrStr_02_42)/2 + (data$HrStr_02_44 + data$HrStr_02_43)/2 +
  (data$HrStr_02_45 + data$HrStr_02_44)/2 + (data$HrStr_02_46 + data$HrStr_02_45)/2 + (data$HrStr_02_47 + data$HrStr_02_46)/2 + (data$HrStr_02_48 + data$HrStr_02_47)/2 + (data$HrStr_02_49 + data$HrStr_02_48)/2 +
  (data$HrStr_02_50 + data$HrStr_02_49)/2 + (data$HrStr_02_51 + data$HrStr_02_50)/2 + (data$HrStr_02_52 + data$HrStr_02_51)/2 + (data$HrStr_02_53 + data$HrStr_02_52)/2 + (data$HrStr_02_54 + data$HrStr_02_53)/2 +
  (data$HrStr_02_55 + data$HrStr_02_54)/2 + (data$HrStr_02_56 + data$HrStr_02_55)/2 + (data$HrStr_02_57 + data$HrStr_02_56)/2 + (data$HrStr_02_58 + data$HrStr_02_57)/2 + (data$HrStr_02_59 + data$HrStr_02_58)/2 +
  (data$HrStr_03_01 + data$HrStr_03_00)/2 + (data$HrStr_03_02 + data$HrStr_03_01)/2 + (data$HrStr_03_03 + data$HrStr_03_02)/2 + (data$HrStr_03_04 + data$HrStr_03_03)/2 +
  (data$HrStr_03_05 + data$HrStr_03_04)/2 + (data$HrStr_03_06 + data$HrStr_03_05)/2 + (data$HrStr_03_07 + data$HrStr_03_06)/2 + (data$HrStr_03_08 + data$HrStr_03_07)/2 + (data$HrStr_03_09 + data$HrStr_03_08)/2 + 
  (data$HrStr_03_10 + data$HrStr_03_09)/2 + (data$HrStr_03_11 + data$HrStr_03_10)/2 + (data$HrStr_03_12 + data$HrStr_03_11)/2 + (data$HrStr_03_13 + data$HrStr_03_12)/2 + (data$HrStr_03_14 + data$HrStr_03_13)/2 +
  (data$HrStr_03_15 + data$HrStr_03_14)/2 + (data$HrStr_03_16 + data$HrStr_03_15)/2 + (data$HrStr_03_17 + data$HrStr_03_16)/2 + (data$HrStr_03_18 + data$HrStr_03_17)/2 + (data$HrStr_03_19 + data$HrStr_03_18)/2 +
  (data$HrStr_03_20 + data$HrStr_03_19)/2 + (data$HrStr_03_21 + data$HrStr_03_20)/2 + (data$HrStr_03_22 + data$HrStr_03_21)/2 + (data$HrStr_03_23 + data$HrStr_03_22)/2 + (data$HrStr_03_24 + data$HrStr_03_23)/2 +
  (data$HrStr_03_25 + data$HrStr_03_24)/2 + (data$HrStr_03_26 + data$HrStr_03_25)/2 + (data$HrStr_03_27 + data$HrStr_03_26)/2 + (data$HrStr_03_28 + data$HrStr_03_27)/2 + (data$HrStr_03_29 + data$HrStr_03_28)/2 +
  (data$HrStr_03_30 + data$HrStr_03_29)/2 + (data$HrStr_03_31 + data$HrStr_03_30)/2 + (data$HrStr_03_32 + data$HrStr_03_31)/2 + (data$HrStr_03_33 + data$HrStr_03_32)/2 + (data$HrStr_03_34 + data$HrStr_03_33)/2 +
  (data$HrStr_03_35 + data$HrStr_03_34)/2 + (data$HrStr_03_36 + data$HrStr_03_35)/2 + (data$HrStr_03_37 + data$HrStr_03_36)/2 + (data$HrStr_03_38 + data$HrStr_03_37)/2 + (data$HrStr_03_39 + data$HrStr_03_38)/2 +
  (data$HrStr_03_40 + data$HrStr_03_39)/2 + (data$HrStr_03_41 + data$HrStr_03_40)/2 + (data$HrStr_03_42 + data$HrStr_03_41)/2 + (data$HrStr_03_43 + data$HrStr_03_42)/2 + (data$HrStr_03_44 + data$HrStr_03_43)/2 +
  (data$HrStr_03_45 + data$HrStr_03_44)/2 + (data$HrStr_03_46 + data$HrStr_03_45)/2 + (data$HrStr_03_47 + data$HrStr_03_46)/2 + (data$HrStr_03_48 + data$HrStr_03_47)/2 + (data$HrStr_03_49 + data$HrStr_03_48)/2 +
  (data$HrStr_03_50 + data$HrStr_03_49)/2 + (data$HrStr_03_51 + data$HrStr_03_50)/2 + (data$HrStr_03_52 + data$HrStr_03_51)/2 + (data$HrStr_03_53 + data$HrStr_03_52)/2 + (data$HrStr_03_54 + data$HrStr_03_53)/2 +
  (data$HrStr_03_55 + data$HrStr_03_54)/2 + (data$HrStr_03_56 + data$HrStr_03_55)/2 + (data$HrStr_03_57 + data$HrStr_03_56)/2 + (data$HrStr_03_58 + data$HrStr_03_57)/2 + (data$HrStr_03_59 + data$HrStr_03_58)/2 

data$SSHRCombAUCi <- data$SSHRCombAUCg - (239 * data$HRbaseline)

SC Combined

data$SSSCCombAUCg <-(data$ScStr_00_01 + data$ScStr_00_00)/2 + (data$ScStr_00_02 + data$ScStr_00_01)/2 + (data$ScStr_00_03 + data$ScStr_00_02)/2 + (data$ScStr_00_04 + data$ScStr_00_03)/2 +
  (data$ScStr_00_05 + data$ScStr_00_04)/2 + (data$ScStr_00_06 + data$ScStr_00_05)/2 + (data$ScStr_00_07 + data$ScStr_00_06)/2 + (data$ScStr_00_08 + data$ScStr_00_07)/2 + (data$ScStr_00_09 + data$ScStr_00_08)/2 + 
  (data$ScStr_00_10 + data$ScStr_00_09)/2 + (data$ScStr_00_11 + data$ScStr_00_10)/2 + (data$ScStr_00_12 + data$ScStr_00_11)/2 + (data$ScStr_00_13 + data$ScStr_00_12)/2 + (data$ScStr_00_14 + data$ScStr_00_13)/2 +
  (data$ScStr_00_15 + data$ScStr_00_14)/2 + (data$ScStr_00_16 + data$ScStr_00_15)/2 + (data$ScStr_00_17 + data$ScStr_00_16)/2 + (data$ScStr_00_18 + data$ScStr_00_17)/2 + (data$ScStr_00_19 + data$ScStr_00_18)/2 +
  (data$ScStr_00_20 + data$ScStr_00_19)/2 + (data$ScStr_00_21 + data$ScStr_00_20)/2 + (data$ScStr_00_22 + data$ScStr_00_21)/2 + (data$ScStr_00_23 + data$ScStr_00_22)/2 + (data$ScStr_00_24 + data$ScStr_00_23)/2 +
  (data$ScStr_00_25 + data$ScStr_00_24)/2 + (data$ScStr_00_26 + data$ScStr_00_25)/2 + (data$ScStr_00_27 + data$ScStr_00_26)/2 + (data$ScStr_00_28 + data$ScStr_00_27)/2 + (data$ScStr_00_29 + data$ScStr_00_28)/2 +
  (data$ScStr_00_30 + data$ScStr_00_29)/2 + (data$ScStr_00_31 + data$ScStr_00_30)/2 + (data$ScStr_00_32 + data$ScStr_00_31)/2 + (data$ScStr_00_33 + data$ScStr_00_32)/2 + (data$ScStr_00_34 + data$ScStr_00_33)/2 +
  (data$ScStr_00_35 + data$ScStr_00_34)/2 + (data$ScStr_00_36 + data$ScStr_00_35)/2 + (data$ScStr_00_37 + data$ScStr_00_36)/2 + (data$ScStr_00_38 + data$ScStr_00_37)/2 + (data$ScStr_00_39 + data$ScStr_00_38)/2 +
  (data$ScStr_00_40 + data$ScStr_00_39)/2 + (data$ScStr_00_41 + data$ScStr_00_40)/2 + (data$ScStr_00_42 + data$ScStr_00_41)/2 + (data$ScStr_00_43 + data$ScStr_00_42)/2 + (data$ScStr_00_44 + data$ScStr_00_43)/2 +
  (data$ScStr_00_45 + data$ScStr_00_44)/2 + (data$ScStr_00_46 + data$ScStr_00_45)/2 + (data$ScStr_00_47 + data$ScStr_00_46)/2 + (data$ScStr_00_48 + data$ScStr_00_47)/2 + (data$ScStr_00_49 + data$ScStr_00_48)/2 +
  (data$ScStr_00_50 + data$ScStr_00_49)/2 + (data$ScStr_00_51 + data$ScStr_00_50)/2 + (data$ScStr_00_52 + data$ScStr_00_51)/2 + (data$ScStr_00_53 + data$ScStr_00_52)/2 + (data$ScStr_00_54 + data$ScStr_00_53)/2 +
  (data$ScStr_00_55 + data$ScStr_00_54)/2 + (data$ScStr_00_56 + data$ScStr_00_55)/2 + (data$ScStr_00_57 + data$ScStr_00_56)/2 + (data$ScStr_00_58 + data$ScStr_00_57)/2 + (data$ScStr_00_59 + data$ScStr_00_58)/2 +
  (data$ScStr_01_00 + data$ScStr_00_59)/2 + 
  (data$ScStr_01_01 + data$ScStr_01_00)/2 + (data$ScStr_01_02 + data$ScStr_01_01)/2 + (data$ScStr_01_03 + data$ScStr_01_02)/2 + (data$ScStr_01_04 + data$ScStr_01_03)/2 +
  (data$ScStr_01_05 + data$ScStr_01_04)/2 + (data$ScStr_01_06 + data$ScStr_01_05)/2 + (data$ScStr_01_07 + data$ScStr_01_06)/2 + (data$ScStr_01_08 + data$ScStr_01_07)/2 + (data$ScStr_01_09 + data$ScStr_01_08)/2 + 
  (data$ScStr_01_10 + data$ScStr_01_09)/2 + (data$ScStr_01_11 + data$ScStr_01_10)/2 + (data$ScStr_01_12 + data$ScStr_01_11)/2 + (data$ScStr_01_13 + data$ScStr_01_12)/2 + (data$ScStr_01_14 + data$ScStr_01_13)/2 +
  (data$ScStr_01_15 + data$ScStr_01_14)/2 + (data$ScStr_01_16 + data$ScStr_01_15)/2 + (data$ScStr_01_17 + data$ScStr_01_16)/2 + (data$ScStr_01_18 + data$ScStr_01_17)/2 + (data$ScStr_01_19 + data$ScStr_01_18)/2 +
  (data$ScStr_01_20 + data$ScStr_01_19)/2 + (data$ScStr_01_21 + data$ScStr_01_20)/2 + (data$ScStr_01_22 + data$ScStr_01_21)/2 + (data$ScStr_01_23 + data$ScStr_01_22)/2 + (data$ScStr_01_24 + data$ScStr_01_23)/2 +
  (data$ScStr_01_25 + data$ScStr_01_24)/2 + (data$ScStr_01_26 + data$ScStr_01_25)/2 + (data$ScStr_01_27 + data$ScStr_01_26)/2 + (data$ScStr_01_28 + data$ScStr_01_27)/2 + (data$ScStr_01_29 + data$ScStr_01_28)/2 +
  (data$ScStr_01_30 + data$ScStr_01_29)/2 + (data$ScStr_01_31 + data$ScStr_01_30)/2 + (data$ScStr_01_32 + data$ScStr_01_31)/2 + (data$ScStr_01_33 + data$ScStr_01_32)/2 + (data$ScStr_01_34 + data$ScStr_01_33)/2 +
  (data$ScStr_01_35 + data$ScStr_01_34)/2 + (data$ScStr_01_36 + data$ScStr_01_35)/2 + (data$ScStr_01_37 + data$ScStr_01_36)/2 + (data$ScStr_01_38 + data$ScStr_01_37)/2 + (data$ScStr_01_39 + data$ScStr_01_38)/2 +
  (data$ScStr_01_40 + data$ScStr_01_39)/2 + (data$ScStr_01_41 + data$ScStr_01_40)/2 + (data$ScStr_01_42 + data$ScStr_01_41)/2 + (data$ScStr_01_43 + data$ScStr_01_42)/2 + (data$ScStr_01_44 + data$ScStr_01_43)/2 +
  (data$ScStr_01_45 + data$ScStr_01_44)/2 + (data$ScStr_01_46 + data$ScStr_01_45)/2 + (data$ScStr_01_47 + data$ScStr_01_46)/2 + (data$ScStr_01_48 + data$ScStr_01_47)/2 + (data$ScStr_01_49 + data$ScStr_01_48)/2 +
  (data$ScStr_01_50 + data$ScStr_01_49)/2 + (data$ScStr_01_51 + data$ScStr_01_50)/2 + (data$ScStr_01_52 + data$ScStr_01_51)/2 + (data$ScStr_01_53 + data$ScStr_01_52)/2 + (data$ScStr_01_54 + data$ScStr_01_53)/2 +
  (data$ScStr_01_55 + data$ScStr_01_54)/2 + (data$ScStr_01_56 + data$ScStr_01_55)/2 + (data$ScStr_01_57 + data$ScStr_01_56)/2 + (data$ScStr_01_58 + data$ScStr_01_57)/2 + (data$ScStr_01_59 + data$ScStr_01_58)/2 +
  (data$ScStr_02_00 + data$ScStr_01_59)/2 +
  (data$ScStr_02_01 + data$ScStr_02_00)/2 + (data$ScStr_02_02 + data$ScStr_02_01)/2 + (data$ScStr_02_03 + data$ScStr_02_02)/2 + (data$ScStr_02_04 + data$ScStr_02_03)/2 +
  (data$ScStr_02_05 + data$ScStr_02_04)/2 + (data$ScStr_02_06 + data$ScStr_02_05)/2 + (data$ScStr_02_07 + data$ScStr_02_06)/2 + (data$ScStr_02_08 + data$ScStr_02_07)/2 + (data$ScStr_02_09 + data$ScStr_02_08)/2 + 
  (data$ScStr_02_10 + data$ScStr_02_09)/2 + (data$ScStr_02_11 + data$ScStr_02_10)/2 + (data$ScStr_02_12 + data$ScStr_02_11)/2 + (data$ScStr_02_13 + data$ScStr_02_12)/2 + (data$ScStr_02_14 + data$ScStr_02_13)/2 +
  (data$ScStr_02_15 + data$ScStr_02_14)/2 + (data$ScStr_02_16 + data$ScStr_02_15)/2 + (data$ScStr_02_17 + data$ScStr_02_16)/2 + (data$ScStr_02_18 + data$ScStr_02_17)/2 + (data$ScStr_02_19 + data$ScStr_02_18)/2 +
  (data$ScStr_02_20 + data$ScStr_02_19)/2 + (data$ScStr_02_21 + data$ScStr_02_20)/2 + (data$ScStr_02_22 + data$ScStr_02_21)/2 + (data$ScStr_02_23 + data$ScStr_02_22)/2 + (data$ScStr_02_24 + data$ScStr_02_23)/2 +
  (data$ScStr_02_25 + data$ScStr_02_24)/2 + (data$ScStr_02_26 + data$ScStr_02_25)/2 + (data$ScStr_02_27 + data$ScStr_02_26)/2 + (data$ScStr_02_28 + data$ScStr_02_27)/2 + (data$ScStr_02_29 + data$ScStr_02_28)/2 +
  (data$ScStr_02_30 + data$ScStr_02_29)/2 + (data$ScStr_02_31 + data$ScStr_02_30)/2 + (data$ScStr_02_32 + data$ScStr_02_31)/2 + (data$ScStr_02_33 + data$ScStr_02_32)/2 + (data$ScStr_02_34 + data$ScStr_02_33)/2 +
  (data$ScStr_02_35 + data$ScStr_02_34)/2 + (data$ScStr_02_36 + data$ScStr_02_35)/2 + (data$ScStr_02_37 + data$ScStr_02_36)/2 + (data$ScStr_02_38 + data$ScStr_02_37)/2 + (data$ScStr_02_39 + data$ScStr_02_38)/2 +
  (data$ScStr_02_40 + data$ScStr_02_39)/2 + (data$ScStr_02_41 + data$ScStr_02_40)/2 + (data$ScStr_02_42 + data$ScStr_02_41)/2 + (data$ScStr_02_43 + data$ScStr_02_42)/2 + (data$ScStr_02_44 + data$ScStr_02_43)/2 +
  (data$ScStr_02_45 + data$ScStr_02_44)/2 + (data$ScStr_02_46 + data$ScStr_02_45)/2 + (data$ScStr_02_47 + data$ScStr_02_46)/2 + (data$ScStr_02_48 + data$ScStr_02_47)/2 + (data$ScStr_02_49 + data$ScStr_02_48)/2 +
  (data$ScStr_02_50 + data$ScStr_02_49)/2 + (data$ScStr_02_51 + data$ScStr_02_50)/2 + (data$ScStr_02_52 + data$ScStr_02_51)/2 + (data$ScStr_02_53 + data$ScStr_02_52)/2 + (data$ScStr_02_54 + data$ScStr_02_53)/2 +
  (data$ScStr_02_55 + data$ScStr_02_54)/2 + (data$ScStr_02_56 + data$ScStr_02_55)/2 + (data$ScStr_02_57 + data$ScStr_02_56)/2 + (data$ScStr_02_58 + data$ScStr_02_57)/2 + (data$ScStr_02_59 + data$ScStr_02_58)/2 +
  (data$ScStr_03_00 + data$ScStr_02_59)/2 +
  (data$ScStr_03_01 + data$ScStr_03_00)/2 + (data$ScStr_03_02 + data$ScStr_03_01)/2 + (data$ScStr_03_03 + data$ScStr_03_02)/2 + (data$ScStr_03_04 + data$ScStr_03_03)/2 +
  (data$ScStr_03_05 + data$ScStr_03_04)/2 + (data$ScStr_03_06 + data$ScStr_03_05)/2 + (data$ScStr_03_07 + data$ScStr_03_06)/2 + (data$ScStr_03_08 + data$ScStr_03_07)/2 + (data$ScStr_03_09 + data$ScStr_03_08)/2 + 
  (data$ScStr_03_10 + data$ScStr_03_09)/2 + (data$ScStr_03_11 + data$ScStr_03_10)/2 + (data$ScStr_03_12 + data$ScStr_03_11)/2 + (data$ScStr_03_13 + data$ScStr_03_12)/2 + (data$ScStr_03_14 + data$ScStr_03_13)/2 +
  (data$ScStr_03_15 + data$ScStr_03_14)/2 + (data$ScStr_03_16 + data$ScStr_03_15)/2 + (data$ScStr_03_17 + data$ScStr_03_16)/2 + (data$ScStr_03_18 + data$ScStr_03_17)/2 + (data$ScStr_03_19 + data$ScStr_03_18)/2 +
  (data$ScStr_03_20 + data$ScStr_03_19)/2 + (data$ScStr_03_21 + data$ScStr_03_20)/2 + (data$ScStr_03_22 + data$ScStr_03_21)/2 + (data$ScStr_03_23 + data$ScStr_03_22)/2 + (data$ScStr_03_24 + data$ScStr_03_23)/2 +
  (data$ScStr_03_25 + data$ScStr_03_24)/2 + (data$ScStr_03_26 + data$ScStr_03_25)/2 + (data$ScStr_03_27 + data$ScStr_03_26)/2 + (data$ScStr_03_28 + data$ScStr_03_27)/2 + (data$ScStr_03_29 + data$ScStr_03_28)/2 +
  (data$ScStr_03_30 + data$ScStr_03_29)/2 + (data$ScStr_03_31 + data$ScStr_03_30)/2 + (data$ScStr_03_32 + data$ScStr_03_31)/2 + (data$ScStr_03_33 + data$ScStr_03_32)/2 + (data$ScStr_03_34 + data$ScStr_03_33)/2 +
  (data$ScStr_03_35 + data$ScStr_03_34)/2 + (data$ScStr_03_36 + data$ScStr_03_35)/2 + (data$ScStr_03_37 + data$ScStr_03_36)/2 + (data$ScStr_03_38 + data$ScStr_03_37)/2 + (data$ScStr_03_39 + data$ScStr_03_38)/2 +
  (data$ScStr_03_40 + data$ScStr_03_39)/2 + (data$ScStr_03_41 + data$ScStr_03_40)/2 + (data$ScStr_03_42 + data$ScStr_03_41)/2 + (data$ScStr_03_43 + data$ScStr_03_42)/2 + (data$ScStr_03_44 + data$ScStr_03_43)/2 +
  (data$ScStr_03_45 + data$ScStr_03_44)/2 + (data$ScStr_03_46 + data$ScStr_03_45)/2 + (data$ScStr_03_47 + data$ScStr_03_46)/2 + (data$ScStr_03_48 + data$ScStr_03_47)/2 + (data$ScStr_03_49 + data$ScStr_03_48)/2 +
  (data$ScStr_03_50 + data$ScStr_03_49)/2 + (data$ScStr_03_51 + data$ScStr_03_50)/2 + (data$ScStr_03_52 + data$ScStr_03_51)/2 + (data$ScStr_03_53 + data$ScStr_03_52)/2 + (data$ScStr_03_54 + data$ScStr_03_53)/2 +
  (data$ScStr_03_55 + data$ScStr_03_54)/2 + (data$ScStr_03_56 + data$ScStr_03_55)/2 + (data$ScStr_03_57 + data$ScStr_03_56)/2 + (data$ScStr_03_58 + data$ScStr_03_57)/2 + (data$ScStr_03_59 + data$ScStr_03_58)/2 

data$SSSCCombAUCi <- data$SSSCCombAUCg - (239 * data$SCbaseline)

Difference Scores Countdown

This was calculated using the following:

\(Change Score = T2 (12 \,second \,countdown \,phase) - T1 (last \,5 \,seconds \, of \,rest \, phase)/trial time\)

SC

# Signaled

data$CDSkin5secT1 <- (data$ScStr_00_07 + data$ScStr_00_08 + data$ScStr_00_09 + data$ScStr_00_10 + data$ScStr_00_11)/5

data$CDSkin12secT1 <- (data$ScStr_00_12 + data$ScStr_00_13 + data$ScStr_00_14 + data$ScStr_00_15 + data$ScStr_00_16 + data$ScStr_00_17 +
                         data$ScStr_00_18 + data$ScStr_00_19 + data$ScStr_00_20 + data$ScStr_00_21 + data$ScStr_00_22 + data$ScStr_00_23)/12

data$CDSkinDifT1 <- data$CDSkin12secT1 - data$CDSkin5secT1


data$CDSkin5secT3 <- (data$ScStr_01_37 + data$ScStr_01_38 + data$ScStr_01_39 + data$ScStr_01_40 + data$ScStr_01_41)/5

data$CDSkin12secT3 <- (data$ScStr_01_42 +  data$ScStr_01_43 + data$ScStr_01_44 + data$ScStr_01_45 + data$ScStr_01_46 + data$ScStr_01_47 +
                         data$ScStr_01_48 + data$ScStr_01_49 + data$ScStr_01_50 + data$ScStr_01_51 + data$ScStr_01_52 + data$ScStr_01_53)/12

data$CDSkinDifT3 <- data$CDSkin12secT3 - data$CDSkin5secT3 

# Signaled mean 

data$CDSkinSignal12 <- (data$CDSkinDifT1 + data$CDSkinDifT3)/2



# Unsignaled


data$CDSkin5secT2 <- (data$ScStr_00_52 + data$ScStr_00_53 + data$ScStr_00_54 + data$ScStr_00_55 + data$ScStr_00_56)/5

data$CDSkin12secT2 <- (data$ScStr_00_57 +  data$ScStr_00_58 + data$ScStr_00_59 + data$ScStr_01_00 + data$ScStr_01_01 + data$ScStr_01_02 + data$ScStr_01_03 + data$ScStr_01_04 + data$ScStr_01_05 + data$ScStr_01_06 + data$ScStr_01_07 + data$ScStr_01_08)/12

data$CDSkinDifT2 <- data$CDSkin12secT2 - data$CDSkin5secT2


data$CDSkin5secT4 <- (data$ScStr_02_22 + data$ScStr_02_23 + data$ScStr_02_24 + data$ScStr_02_25 + data$ScStr_02_26)/5

data$CDSkin12secT4 <- (data$ScStr_02_27 + data$ScStr_02_28 + data$ScStr_02_29 + data$ScStr_02_30 + data$ScStr_02_31 + data$ScStr_02_32 + 
                         data$ScStr_02_33 + data$ScStr_02_34 + data$ScStr_02_35 + data$ScStr_02_36 + data$ScStr_02_37 + data$ScStr_02_38)/12

data$CDSkinDifT4 <- data$CDSkin12secT4 - data$CDSkin5secT4

# Unsignaled mean 

data$CDSkinUnsig12 <- (data$CDSkinDifT2 + data$CDSkinDifT4)/2

HR

# Signaled

data$CDHeart5secT1 <- (data$HrStr_00_07 + data$HrStr_00_08 + data$HrStr_00_09 + data$HrStr_00_10 + data$HrStr_00_11)/5


data$CDHeart12secT1 <- (data$HrStr_00_12 + data$HrStr_00_13 + data$HrStr_00_14 + data$HrStr_00_15 + data$HrStr_00_16 + data$HrStr_00_17 + data$HrStr_00_18 + data$HrStr_00_19 + data$HrStr_00_20 + data$HrStr_00_21 + data$HrStr_00_22 + data$HrStr_00_23)/12


data$CDHeartDifT1 <- data$CDHeart12secT1 - data$CDHeart5secT1


data$CDHeart5secT3 <- (data$HrStr_01_37 + data$HrStr_01_38 + data$HrStr_01_39 + data$HrStr_01_40 + data$HrStr_01_41)/5


data$CDHeart12secT3 <- (data$HrStr_01_42 +  data$HrStr_01_43 + data$HrStr_01_44 + data$HrStr_01_45 + data$HrStr_01_46 + data$HrStr_01_47 + data$HrStr_01_48 + data$HrStr_01_49 + data$HrStr_01_50 + data$HrStr_01_51 + data$HrStr_01_52 + data$HrStr_01_53)/12


data$CDHeartDifT3 <- data$CDHeart12secT3 - data$CDHeart5secT3

data$CDHeartSignal12 <- (data$CDHeartDifT1 + data$CDHeartDifT3)/2



# Unsignaled


data$CDHeart5secT2 <- (data$HrStr_00_52 + data$HrStr_00_53 + data$HrStr_00_54 + data$HrStr_00_55 + data$HrStr_00_56)/5


data$CDHeart12secT2 <- (data$HrStr_00_57 +  data$HrStr_00_58 + data$HrStr_00_59 + data$HrStr_01_00 + data$HrStr_01_01 + data$HrStr_01_02 + data$HrStr_01_03 + data$HrStr_01_04 + data$HrStr_01_05 + data$HrStr_01_06 + data$HrStr_01_07 + data$HrStr_01_08)/12


data$CDHeartDifT2 <- data$CDHeart12secT2 - data$CDHeart5secT2


data$CDHeart5secT4 <- (data$HrStr_02_22 + data$HrStr_02_23 + data$HrStr_02_24 + data$HrStr_02_25 + data$HrStr_02_26)/5


data$CDHeart12secT4 <- (data$HrStr_02_27 + data$HrStr_02_28 + data$HrStr_02_29 + data$HrStr_02_30 + data$HrStr_02_31 + data$HrStr_02_32 + data$HrStr_02_33 + data$HrStr_02_34 + data$HrStr_02_35 + data$HrStr_02_36 + data$HrStr_02_37 + data$HrStr_02_38)/12



data$CDHeartDifT4 <- data$CDHeart12secT4 - data$CDHeart5secT4

# Unsignaled mean


data$CDHeartUnsig12 <- (data$CDHeartDifT2 + data$CDHeartDifT4)/2

Countdown Recovery Measures

This was calculated using the following:

\(Recovery Score = (mean \, of \, the \, 12 \, seconds \, after \, noise blast \, – 5 \, mean \, of \, the \,seconds \, before \, the \,noise blast)\)

Heart Rate Change Recovery (HRCR)

# Trial 1

data$HrT112Rec <- (data$HrStr_00_25 + data$HrStr_00_26 + data$HrStr_00_27 + data$HrStr_00_28 + data$HrStr_00_29 + data$HrStr_00_30 + data$HrStr_00_31 + data$HrStr_00_32 + data$HrStr_00_33 + data$HrStr_00_34 + data$HrStr_00_35 + data$HrStr_00_36)/12

data$HrT15Rec <- (data$HrStr_00_19 + data$HrStr_00_20 + data$HrStr_00_21 + data$HrStr_00_22 + data$HrStr_00_23)/5


data$HrT1Recov <- (data$HrT112Rec - data$HrT15Rec)

# Trial 2

data$HrT212Rec <- (data$HrStr_01_10 + data$HrStr_01_11 + data$HrStr_01_12 + data$HrStr_01_13 + data$HrStr_01_14 + data$HrStr_01_15 + data$HrStr_01_16 + data$HrStr_01_17 + data$HrStr_01_18 + data$HrStr_01_19 + data$HrStr_01_20 + data$HrStr_01_21)/12

data$HrT25Rec <- (data$HrStr_01_04 + data$HrStr_01_05 + data$HrStr_01_06 + data$HrStr_01_07 + data$HrStr_01_08)/5


data$HrT2Recov <- (data$HrT212Rec - data$HrT25Rec)



# Trial 3

data$HrT312Rec <- (data$HrStr_01_55 + data$HrStr_01_56 + data$HrStr_01_57 + data$HrStr_01_58 + data$HrStr_01_59 + data$HrStr_02_00 + data$HrStr_02_01 + data$HrStr_02_02 + data$HrStr_02_03 + data$HrStr_02_04 + data$HrStr_02_05 + data$HrStr_02_06)/12

data$HrT35Rec <- (data$HrStr_01_49 + data$HrStr_01_50 + data$HrStr_01_51 + data$HrStr_01_52 + data$HrStr_01_53)/5


data$HrT3Recov <- (data$HrT312Rec - data$HrT35Rec)


# Trial 4

data$HrT412Rec <- (data$HrStr_02_40 + data$HrStr_02_41 + data$HrStr_02_42 + data$HrStr_02_43 + data$HrStr_02_44 + data$HrStr_02_45 + data$HrStr_02_46 + data$HrStr_02_47 + data$HrStr_02_48 + data$HrStr_02_49 + data$HrStr_02_50 + data$HrStr_02_51)/12

data$HrT45Rec <- (data$HrStr_02_34 + data$HrStr_02_35 + data$HrStr_02_36 + data$HrStr_02_37 + data$HrStr_02_38)/5


data$HrT4Recov <- (data$HrT412Rec - data$HrT45Rec)


# Signaled 


data$HrSigRecovMean <- (data$HrT1Recov + data$HrT3Recov)/2

# Unsignaled 

data$HrUnSigRecovMean <- (data$HrT2Recov + data$HrT4Recov)/2

Skin Conductance Level Change Recover (SCLCR)

# Trial 1

data$ScT112Rec <- (data$ScStr_00_25 + data$ScStr_00_26 + data$ScStr_00_27 + data$ScStr_00_28 + data$ScStr_00_29 + data$ScStr_00_30 + data$ScStr_00_31 + data$ScStr_00_32 + data$ScStr_00_33 + data$ScStr_00_34 + data$ScStr_00_35 + data$ScStr_00_36)/12

data$ScT15Rec <- (data$ScStr_00_19 + data$ScStr_00_20 + data$ScStr_00_21 + data$ScStr_00_22 + data$ScStr_00_23)/5


data$ScT1Recov <- (data$ScT112Rec - data$ScT15Rec)

# Trial 2

data$ScT212Rec <- (data$ScStr_01_10 + data$ScStr_01_11 + data$ScStr_01_12 + data$ScStr_01_13 + data$ScStr_01_14 + data$ScStr_01_15 + data$ScStr_01_16 + data$ScStr_01_17 + data$ScStr_01_18 + data$ScStr_01_19 + data$ScStr_01_20 + data$ScStr_01_21)/12

data$ScT25Rec <- (data$ScStr_01_04 + data$ScStr_01_05 + data$ScStr_01_06 + data$ScStr_01_07 + data$ScStr_01_08)/5


data$ScT2Recov <- (data$ScT212Rec - data$ScT25Rec)



# Trial 3

data$ScT312Rec <- (data$ScStr_01_55 + data$ScStr_01_56 + data$ScStr_01_57 + data$ScStr_01_58 + data$ScStr_01_59 + data$ScStr_02_00 + data$ScStr_02_01 + data$ScStr_02_02 + data$ScStr_02_03 + data$ScStr_02_04 + data$ScStr_02_05 + data$ScStr_02_06)/12

data$ScT35Rec <- (data$ScStr_01_49 + data$ScStr_01_50 + data$ScStr_01_51 + data$ScStr_01_52 + data$ScStr_01_53)/5


data$ScT3Recov <- (data$ScT312Rec - data$ScT35Rec)


# Trial 4

data$ScT412Rec <- (data$ScStr_02_40 + data$ScStr_02_41 + data$ScStr_02_42 + data$ScStr_02_43 + data$ScStr_02_44 + data$ScStr_02_45 + data$ScStr_02_46 + data$ScStr_02_47 + data$ScStr_02_48 + data$ScStr_02_49 + data$ScStr_02_50 + data$ScStr_02_51)/12

data$ScT45Rec <- (data$ScStr_02_34 + data$ScStr_02_35 + data$ScStr_02_36 + data$ScStr_02_37 + data$ScStr_02_38)/5


data$ScT4Recov <- (data$ScT412Rec - data$ScT45Rec)

# Signaled 

data$ScSigRecovMean <- (data$ScT1Recov + data$ScT3Recov)/2


# Unsignaled 


data$ScUnSigRecovMean <- (data$ScT2Recov + data$ScT4Recov)/2

Wrangling

Full Sample

Table 1

#Full

FullsampleFinalSurveyT1 <- data |> 
   dplyr::select(Task, Gender, race_eth, race_eth2, White, Male, Female, Age, GenderNumb, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
                 ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
                 LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
                 SSSThrilTotal, SSSExpTotal)

FSFSurveyT1 <- FullsampleFinalSurveyT1 |> 
  na.omit()

FullsampleFinalHRT1 <- data |> 
   dplyr::select(Task, Gender, race_eth, race_eth2, White, Male, Female, Age, GenderNumb, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
                 ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
                 LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
                 SSSThrilTotal, SSSExpTotal, HRbaseline)

FSFHRT1 <- FullsampleFinalHRT1 |> 
  na.omit()


FullsampleFinalSCT1 <- data |> 
  dplyr::select(Task, Gender, race_eth, race_eth2, White, Male, Female, Age, GenderNumb, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal, ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
                 LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
                 SSSThrilTotal, SSSExpTotal, SCbaseline)

FSFSCT1 <- FullsampleFinalSCT1 |> 
  na.omit()

# Social Stressor 

SocialStressorFinalHRT1 <- data |> 
   dplyr::select(Task, White, Gender, Male, Female, Age, GenderNumb, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal, ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
                 LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
                 SSSThrilTotal, SSSExpTotal, SSHRCombAUCi, HRbaseline) |> 
   filter(Task == "2")

SSFHRT1 <- SocialStressorFinalHRT1 |> 
  na.omit()


SocialStressorFinalSCT1 <- data |> 
  dplyr::select(Task, White, Gender, Male, Female, Age, GenderNumb,SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal, ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
                 LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
                 SSSThrilTotal, SSSExpTotal, SSSCCombAUCi, SCbaseline) |> 
  filter(Task == "2")

SSFSCT1 <- SocialStressorFinalSCT1 |> 
  na.omit()


# Countdown 

CountdownFinalHRT1 <- data |> 
   dplyr::select(Task, Male, Female, White, Gender, Age, GenderNumb, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal, ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
                 LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
                 SSSThrilTotal, SSSExpTotal, CDHeartSignal12, CDHeartUnsig12, HrSigRecovMean, HrUnSigRecovMean, HRbaseline) |> 
   filter(Task == "1")

CDFHRT1 <- CountdownFinalHRT1 |> 
   na.omit()
  
CountdownFinalSCT1 <- data |> 
   dplyr::select(Task, Male, Female, White, Gender, Age, GenderNumb, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal, ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
                 LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal, SSSThrilTotal, SSSExpTotal,
                 CDSkinSignal12, CDSkinUnsig12, ScSigRecovMean, ScUnSigRecovMean, SCbaseline) |> 
   filter(Task == "1")
 
CDFSCT1 <- CountdownFinalSCT1 |> 
   na.omit()

Male Only

Table 1

These data frames were required to compensate for the missing variables. If I just selected the one column I needed (e.g.,“HRbaseline”) the missing would not match the true sample number because missing values are contained within the survey. This is most evident in the female sample (Female Only Table 1 code chunk).

# Baseline

MaleHRbaseT1 <- data |> 
  dplyr::select(GenderNumb, White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
                 ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
                 LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
                 SSSThrilTotal, SSSExpTotal, HRbaseline) |> 
  filter(GenderNumb == "2")

MHRbT1 <- MaleHRbaseT1 |> 
  na.omit()


MaleSCbaseT1 <- data |> 
  dplyr::select(GenderNumb, White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
                 ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
                 LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
                 SSSThrilTotal, SSSExpTotal, SCbaseline) |> 
  filter(GenderNumb == "2")

MSCbT1 <- MaleSCbaseT1 |> 
  na.omit()


# Social Stressor 

MaleSSHRT1 <- SocialStressorFinalHRT1 |> 
  dplyr::select(GenderNumb, White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
                 ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
                 LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
                 SSSThrilTotal, SSSExpTotal, SSHRCombAUCi) |>  
  filter(GenderNumb == "2")

MSSHRT1 <- MaleSSHRT1 |> 
  na.omit()

MaleSSSCT1 <- SocialStressorFinalSCT1 |> 
  dplyr::select(GenderNumb, White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
                 ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
                 LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
                 SSSThrilTotal, SSSExpTotal, SSSCCombAUCi) |> 
  filter(GenderNumb == "2")

MSSSCT1 <- MaleSSSCT1 |> 
  na.omit()

# Countdown 


MaleCDHRT1 <- CountdownFinalHRT1 |> 
  dplyr::select(GenderNumb,  White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
                 ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
                 LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
                 SSSThrilTotal, SSSExpTotal, CDHeartSignal12, CDHeartUnsig12, HrSigRecovMean, HrUnSigRecovMean) |> 
  filter(GenderNumb == "2")

MCDHRT1 <- MaleCDHRT1 |> 
  na.omit()

MaleCDSCT1 <- CountdownFinalSCT1 |> 
  dplyr::select(GenderNumb,  White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
                 ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
                 LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
                 SSSThrilTotal, SSSExpTotal, CDSkinSignal12, CDSkinUnsig12, ScSigRecovMean, ScUnSigRecovMean) |>  
  filter(GenderNumb == "2")

MCDSCT1 <- MaleCDSCT1 |> 
  na.omit()

Female Only

# Survey only for distribution checks 

FemaleDistribCheck <-  data |> 
  dplyr::select(GenderNumb, White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
                 ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
                 LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
                 SSSThrilTotal, SSSExpTotal) |> 
  filter(GenderNumb == "1")


FemaleDisCheck <- FemaleDistribCheck |> 
  na.omit()

Table 1

# baseline 

FemaleHRbaselineT1 <-  data |> 
  dplyr::select(GenderNumb, White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
                 ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
                 LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
                 SSSThrilTotal, SSSExpTotal, HRbaseline) |> 
  filter(GenderNumb == "1")


FemaleHRbaseT1 <- FemaleHRbaselineT1 |> 
  na.omit()

FemaleSCbaselineT1 <- data |> 
  dplyr::select(GenderNumb, White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
                 ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
                 LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
                 SSSThrilTotal, SSSExpTotal, SCbaseline) |> 
  filter(GenderNumb == "1")

FemaleSCbaseT1 <- FemaleSCbaselineT1 |> 
  na.omit()

# Social Stressor 

FemaleSocialSHRT1 <- SocialStressorFinalHRT1 |> 
  dplyr::select(GenderNumb, White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
                 ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
                 LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
                 SSSThrilTotal, SSSExpTotal, SSHRCombAUCi) |> 
  filter(GenderNumb == "1")

FemaleSSHRT1 <- FemaleSocialSHRT1 |> 
  na.omit()


FemaleSocialSSCT1 <- SocialStressorFinalSCT1 |> 
  dplyr::select(GenderNumb, White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
                 ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
                 LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
                 SSSThrilTotal, SSSExpTotal, SSSCCombAUCi) |> 
  filter(GenderNumb == "1")

FemaleSCSST1 <- FemaleSocialSSCT1 |> 
  na.omit()

# Countdown 

FemaleHRCountDT1 <- CountdownFinalHRT1 |> 
  dplyr::select(GenderNumb, White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
                 ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
                 LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
                 SSSThrilTotal, SSSExpTotal, CDHeartSignal12, CDHeartUnsig12, HrSigRecovMean, HrUnSigRecovMean) |> 
  filter(GenderNumb == "1")

FemaleHRCDT1 <- FemaleHRCountDT1 |> 
  na.omit()



FemaleSCCountDT1 <- CountdownFinalSCT1 |> 
  dplyr::select(GenderNumb, White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
                 ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
                 LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
                 SSSThrilTotal, SSSExpTotal, CDSkinSignal12, CDSkinUnsig12, ScSigRecovMean, ScUnSigRecovMean) |>  
  filter(GenderNumb == "1")

FemaleSCCDT1 <- FemaleSCCountDT1 |> 
  na.omit()

Scale Reliability Data Frame

Created a new data frame that takes into account the sample and the missing data to calculate the alphas. Can’t use original data frome due to the nature of the autonomic data (i.e., contains a varying amount NAs for all participants, therefore can’t na.omit). To save time and reduce error, I used gsub(). Process below to replicate.

Code <- “Copy and paste the code from the total scores”

new_code <- gsub(“data\$”, ““, code) ^ use an escape character (i.e., \) to treat the $ as normal character and not a special expression. Throws in a”” spot.

new_code2 <- gsub(“\+”, “,”, new_code) ^ same story here.

cat(new_code2) ^ Concatenate the variables into a neat string and copy from console to code chunk.

Scaledf <- data |> 
  dplyr::select (SRP_01n , SRP_02n , SRP_03n , SRP_04n ,  SRP5nRev , SRP6nRev , SRP_07n , SRP_08n ,
                 SRP_09n , SRP_10n , SRP11nRev , SRP_12n , SRP_13n , SRP14nRev ,
                  SRP_15n , SRP16nRev , SRP_17n , SRP18nRev , SRP19nRev , SRP_20n , SRP21nRev ,
                  SRP22nRev , SRP23nRev , SRP24nRev , SRP25nRev , SRP26nRev , SRP_27n , SRP_28n , 
                  SRP_29n , SRP_30n , SRP31nRev , SRP_32n , SRP_33n , SRP34nRev ,  SRP_35n , 
                  SRP36nRev ,  SRP_37n , SRP38nRev , SRP_39n , SRP_40n , SRP_41n , SRP_42n , 
                  SRP_43n , SRP44nRev , SRP_45n , SRP46nRev , SRP47nRev , SRP_48n , SRP_49n , 
                  SRP_50n , SRP_51n , SRP_52n , SRP_53n , SRP_54n , SRP_55n ,  SRP_56n ,
                  SRP_57n , SRP_58n , SRP_59n , SRP_60n , SRP61nRev , SRP_62n , SRP_63n , SRP_64n ,
                  ICU_1nRev , ICU_2n , ICU_3nRev , ICU_4n , ICU_5nRev , ICU_6n , 
                  ICU_7n , ICU_8nRev , ICU_9n , ICU_10n , ICU_11n , ICU_12n , ICU_13nRev ,
                  ICU_14nRev , ICU_15nRev , ICU_16nRev , ICU_17nRev , ICU_18n , ICU_19nRev ,
                  ICU_20n , ICU_21n , ICU_22n , ICU_23nRev , ICU_24nRev ,
                  Lev_01n , Lev_02n , Lev_03nRev , Lev_04n , Lev_05n , Lev_06n , Lev_07nRev , Lev_08n ,
                 Lev_09n , Lev_10nRev , Lev_11n , Lev_12n , Lev_13nRev , Lev_16n , Lev_17n , Lev_18n ,
                 Lev_19n , Lev_20n , Lev_21nRev , Lev_22n , Lev_23n , Lev_24n , Lev_25n , Lev_26nRev , 
                 ZSSS_1nRev , ZSSS_2n , ZSSS_3nRev , ZSSS_4n , ZSSS_5nRev , ZSSS_6nRev , ZSSS_7n , ZSSS_8nRev ,
                 ZSSS_9nRev , ZSSS_10n , ZSSS_11n , ZSSS_12n , ZSSS_13n , ZSSS_14nRev , ZSSS_15n , ZSSS_16nRev , 
                 ZSSS_17nRev , ZSSS_18nRev , ZSSS_19n , ZSSS_20n , ZSSS_21n , ZSSS_22nRev , ZSSS_23nRev , ZSSS_24nRev , 
                 ZSSS_25n , ZSSS_26n , ZSSS_27n , ZSSS_28nRev , ZSSS_29nRev , ZSSS_30n , ZSSS_31n ,
                  ZSSS_32nRev , ZSSS_33n , ZSSS_34nRev , ZSSS_35n , ZSSS_36nRev , ZSSS_37n , ZSSS_38n , ZSSS_39nRev , ZSSS_40n) |> 
  na.omit()
                  
                  

# SRP 

SRPTotA <-Scaledf[ , c("SRP_03n","SRP_08n","SRP_13n","SRP16nRev","SRP_20n","SRP24nRev","SRP_27n",
                    "SRP31nRev","SRP_35n", "SRP38nRev","SRP_41n", "SRP_45n","SRP_50n", "SRP_54n","SRP_58n", "SRP61nRev",
                    "SRP_02n","SRP_07n", "SRP11nRev", "SRP_15n", "SRP19nRev", "SRP23nRev", "SRP26nRev", "SRP_30n", "SRP_33n",
                    "SRP_37n", "SRP_40n", "SRP44nRev", "SRP_48n", "SRP_01n", "SRP_53n", "SRP_56n", "SRP_60n", "SRP_04n", 
                    "SRP_09n", "SRP14nRev", "SRP_17n", "SRP22nRev", "SRP25nRev", "SRP_28n", "SRP_32n", "SRP36nRev", "SRP_39n",
                    "SRP_42n", "SRP47nRev", "SRP_51n", "SRP_55n", "SRP_59n", "SRP5nRev", "SRP6nRev", "SRP_10n", "SRP_12n", 
                    "SRP18nRev", "SRP21nRev", "SRP_29n", "SRP34nRev", "SRP_43n", "SRP46nRev", "SRP_49n", "SRP_52n",
                    "SRP_57n", "SRP_62n", "SRP_63n", "SRP_64n")]

SRPIPMA <-Scaledf[ , c("SRP_03n","SRP_08n","SRP_13n","SRP16nRev","SRP_20n","SRP24nRev","SRP_27n",
                    "SRP31nRev","SRP_35n", "SRP38nRev","SRP_41n", "SRP_45n","SRP_50n", "SRP_54n","SRP_58n", "SRP61nRev")]

SRPICAA <-Scaledf[ , c("SRP_02n","SRP_07n","SRP11nRev","SRP_15n","SRP19nRev","SRP23nRev","SRP26nRev",
                    "SRP_30n","SRP_33n", "SRP_37n","SRP_40n", "SRP44nRev","SRP_48n", "SRP_53n","SRP_56n", "SRP_60n")]

SRPELSA <-Scaledf[ , c("SRP_01n","SRP_04n","SRP_09n","SRP14nRev","SRP_17n","SRP22nRev","SRP25nRev",
                    "SRP_28n","SRP_32n", "SRP36nRev","SRP_39n", "SRP_42n","SRP47nRev", "SRP_51n","SRP_55n", "SRP_59n")]

SRPASBA <-Scaledf[ , c("SRP5nRev","SRP6nRev","SRP_10n","SRP_12n","SRP18nRev","SRP21nRev","SRP_29n",
                    "SRP34nRev","SRP_43n", "SRP46nRev","SRP_49n", "SRP_52n","SRP_57n", "SRP_62n","SRP_63n", "SRP_64n")]

#  ICU 

ICUTotA <-Scaledf[ , c("ICU_1nRev","ICU_2n","ICU_3nRev","ICU_4n","ICU_5nRev","ICU_6n","ICU_7n",
                    "ICU_8nRev","ICU_9n", "ICU_10n", "ICU_11n","ICU_12n", "ICU_13nRev","ICU_14nRev", "ICU_15nRev",
                    "ICU_16nRev","ICU_17nRev", "ICU_18n","ICU_19nRev", "ICU_20n","ICU_21n", "ICU_22n","ICU_23nRev", "ICU_24nRev")]

ICUCalA <-Scaledf[ , c("ICU_4n","ICU_8nRev","ICU_9n","ICU_18n","ICU_11n","ICU_21n","ICU_7n",
                    "ICU_20n","ICU_2n", "ICU_12n","ICU_10n")]

ICUUncareA <-Scaledf[ , c("ICU_15nRev","ICU_23nRev","ICU_16nRev","ICU_3nRev","ICU_17nRev","ICU_24nRev","ICU_13nRev",
                       "ICU_5nRev")]

ICUUnemoA <-Scaledf[ , c("ICU_1nRev","ICU_19nRev","ICU_6n","ICU_22n","ICU_14nRev")]

# LSRP

LevTotA <-Scaledf[ , c("Lev_01n","Lev_02n","Lev_03nRev","Lev_04n","Lev_05n","Lev_06n","Lev_07nRev",
                    "Lev_08n","Lev_09n", "Lev_10nRev","Lev_11n","Lev_12n", "Lev_13nRev","Lev_16n","Lev_17n", "Lev_18n",
                    "Lev_19n", "Lev_20n","Lev_21nRev", "Lev_22n","Lev_23n", "Lev_24n", "Lev_25n","Lev_26nRev" )]

LevPrimA <-Scaledf[ , c("Lev_02n","Lev_04n","Lev_07nRev","Lev_09n","Lev_11n","Lev_12n",
                     "Lev_13nRev","Lev_17n", "Lev_19n","Lev_21nRev","Lev_22n", "Lev_23n","Lev_24n","Lev_25n", "Lev_26nRev")]

LevSecA <-Scaledf[ , c("Lev_01n","Lev_03nRev","Lev_05n","Lev_06n","Lev_08n","Lev_10nRev",
                    "Lev_16n","Lev_18n", "Lev_20n")]
# SSS 

SSSTotA <-Scaledf[ , c("ZSSS_1nRev","ZSSS_2n","ZSSS_3nRev","ZSSS_4n","ZSSS_5nRev","ZSSS_6nRev","ZSSS_7n", "ZSSS_8nRev","ZSSS_9nRev", "ZSSS_10n",
                    "ZSSS_11n", "ZSSS_12n","ZSSS_13n", "ZSSS_14nRev","ZSSS_15n", "ZSSS_16nRev", "ZSSS_17nRev","ZSSS_18nRev", "ZSSS_19n", "ZSSS_20n",
                    "ZSSS_21n", "ZSSS_22nRev", "ZSSS_23nRev", "ZSSS_24nRev", "ZSSS_25n", "ZSSS_26n", "ZSSS_27n","ZSSS_28nRev","ZSSS_29nRev","ZSSS_30n","ZSSS_31n",
                    "ZSSS_32nRev","ZSSS_33n","ZSSS_34nRev","ZSSS_35n","ZSSS_36nRev","ZSSS_37n","ZSSS_38n", "ZSSS_39nRev", "ZSSS_40n")]

SSSDISA <-Scaledf[ , c("ZSSS_12n","ZSSS_13n","ZSSS_25n","ZSSS_30n","ZSSS_33n","ZSSS_35n",
                    "ZSSS_1nRev","ZSSS_29nRev", "ZSSS_32nRev", "ZSSS_36nRev")]

SSSBorA <-Scaledf[ , c("ZSSS_2n","ZSSS_7n","ZSSS_15n","ZSSS_27n","ZSSS_31n","ZSSS_5nRev",
                    "ZSSS_8nRev","ZSSS_24nRev", "ZSSS_34nRev", "ZSSS_39nRev")]

SSSThrilA <-Scaledf[ , c("ZSSS_11n","ZSSS_20n","ZSSS_21n","ZSSS_38n","ZSSS_40n","ZSSS_3nRev",
                      "ZSSS_16nRev","ZSSS_17nRev", "ZSSS_23nRev", "ZSSS_28nRev")]

SSSExpA <-Scaledf[ , c("ZSSS_4n","ZSSS_10n","ZSSS_19n","ZSSS_26n","ZSSS_37n","ZSSS_6nRev",
                    "ZSSS_9nRev","ZSSS_14nRev", "ZSSS_18nRev", "ZSSS_22nRev")]

Graph Data Wrangling

Social Stressor

SSSTGraphHR <- data |> 
  dplyr::select(Task, HrStr_00_00:HrStr_03_59) |> 
  dplyr::filter(Task == "2")

SSSTGraph1 <- SSSTGraphHR |> 
  dplyr::select(HrStr_00_00:HrStr_03_59)

Function 1

Creates a new data frame with the mean of every 5 columns.

SSST_Five <- function(df) {
  # This is a check to ensure that the number of columns is divisible by 5
  if (ncol(df) %% 5 != 0) {
    stop("Something is off.")
  }
  # Create an empty list to store the mean columns
  mean_cols <- list()
  # Loop through (or processes) the columns in groups of 5
  for (i in seq(1, ncol(df), by = 5)) {
    # Calculate the mean for each group of 5 columns
    mean_col <- rowMeans(df[, i:(i + 4)], na.rm = TRUE)
    # Add the mean column to the list
   mean_cols[[as.character((i - 1) / 5 + 1)]] <- mean_col
  }
  # Combine the original data frame with the new mean columns
  df_means <- as.data.frame(mean_cols)
  return(df_means)
}
SSSTVizHR <- SSST_Five(SSSTGraph1)

mean_values <- colMeans(SSSTVizHR, na.rm = TRUE)


data12 <- data.frame(
 time = seq_len(length(mean_values)),
  value = mean_values
)
SSSTGraphSc <- data |> 
  dplyr::select(Task, ScStr_00_00:ScStr_03_59) |> 
  dplyr::filter(Task == "2")

SSSTGraph2 <- SSSTGraphSc |> 
  dplyr::select(ScStr_00_00:ScStr_03_59)

SSSTVizSc <- SSST_Five(SSSTGraph2)

mean_value <- colMeans(SSSTVizSc, na.rm = TRUE)


data123 <- data.frame(
 time = seq_len(length(mean_values)),
  value = mean_value
)

Countdown

CDGraphHR <- data |> 
  dplyr::select(Task, HrStr_00_07:HrStr_00_37, HrStr_01_37: HrStr_02_07,
                HrStr_00_52:HrStr_01_22, HrStr_02_22:HrStr_02_52) |> 
  dplyr::filter(Task == "1")


CDGraphSC <- data |> 
  dplyr::select(Task, ScStr_00_07:ScStr_00_37, ScStr_01_37: ScStr_02_07,
                ScStr_00_52:ScStr_01_22, ScStr_02_22:ScStr_02_52) |> 
  dplyr::filter(Task == "1")


# HR

CDGraphHRSig <- CDGraphHR |> 
  dplyr::select(HrStr_00_07:HrStr_00_37, HrStr_01_37:HrStr_02_07)

CDGraphHRUnsig <- CDGraphHR |> 
  dplyr::select(HrStr_00_52:HrStr_01_22, HrStr_02_22:HrStr_02_52)

# SC

CDGraphSCSig <- CDGraphSC |> 
  dplyr::select(ScStr_00_07:ScStr_00_37, ScStr_01_37:ScStr_02_07)

CDGraphSCUnsig <- CDGraphSC |>
  dplyr::select(ScStr_00_52:ScStr_01_22, ScStr_02_22:ScStr_02_52)

Function 2

Function that divides the data frame in half then creates a new column with the mean of the two columns (i.e., column 1 and column 32, column 2 and column 33, etc.).

Mean_Phase <- function(df) {
  # Run a check
  num_cols <- ncol(df)
  if (num_cols %% 2 != 0) {
    stop("Something is off.")
  }
  # Loop through half of the columns to create the mean columns
  for (i in 1:(num_cols / 2)) {
    col1 <- df[, i]
    col2 <- df[, i + (num_cols / 2)]
    new_col_name <- paste0("Mean_", names(df)[i], "_", names(df)[i + (num_cols / 2)])
    df[[new_col_name]] <- rowMeans(cbind(col1, col2))
  }
  
  return(df)
}
# HR Signaled 
CDGraphHRSig1 <- Mean_Phase(CDGraphHRSig)

CDGraphHRSignaled <- CDGraphHRSig1 |> 
  dplyr::select(cols = 63:93)

# HR Unsignaled
CDGraphHRUnsig1 <- Mean_Phase(CDGraphHRUnsig)

CDGraphHRUnsignaled <- CDGraphHRUnsig1 |> 
  dplyr::select(cols = 63:93)

# SC Signaled
CDGraphSCSig1 <- Mean_Phase(CDGraphSCSig)

CDGraphSCSignaled <- CDGraphSCSig1 |> 
  dplyr::select(cols = 63:93)

# SC Unsignaled
CDGraphSCUnsig1 <- Mean_Phase(CDGraphSCUnsig)

CDGraphSCUnsignaled <- CDGraphSCUnsig1 |> 
  dplyr::select(cols = 63:93)

HR

# HRSig 

mean_values <- colMeans(CDGraphHRSignaled, na.rm = TRUE)

# Convert mean_values to a data frame for plotting
CDGraphHRSignaled1 <- data.frame(
 time = seq_len(length(mean_values)),
  value = mean_values)


# HRUnsig

mean_values <- colMeans(CDGraphHRUnsignaled, na.rm = TRUE)

CDGraphHRUnsignaled1 <- data.frame(
 time = seq_len(length(mean_values)),
  value = mean_values)


# Merge the two 

CDGraphHR <- dplyr::bind_rows(
  CDGraphHRSignaled1 |> 
    dplyr::mutate(group = "Signaled"),
  CDGraphHRUnsignaled1 |> 
    dplyr::mutate(group = "Unsignaled")
)

# Reset rownames 
rownames(CDGraphHR) <- 1:nrow(CDGraphHR)

SC

# SCSig

mean_values <- colMeans(CDGraphSCSignaled, na.rm = TRUE)

CDGraphSCSignaled1 <- data.frame(
   time = seq_len(length(mean_values)),
  value = mean_values)


# SCUnsig


mean_values <- colMeans(CDGraphSCUnsignaled, na.rm = TRUE)

CDGraphSCUnsignaled1 <- data.frame(
  time = seq_len(length(mean_values)),
  value = mean_values)

                                         
# Merge the two

CDGraphSC <- dplyr::bind_rows(
  CDGraphSCSignaled1 |> 
    dplyr::mutate(group = "Signaled"),
  CDGraphSCUnsignaled1 |> 
    dplyr::mutate(group = "Unsignaled")
)

# Reset rownames 
rownames(CDGraphSC) <- 1:nrow(CDGraphSC)

Analysis

Reliabity Scores

Note: ICU callousness, SSS boredom and experience seeking were dropped because of their low alpha (02-09-24).

# SRP

cronbach.alpha(SRPTotA)

Cronbach's alpha for the 'SRPTotA' data-set

Items: 64
Sample units: 92
alpha: 0.884
cronbach.alpha(SRPIPMA)

Cronbach's alpha for the 'SRPIPMA' data-set

Items: 16
Sample units: 92
alpha: 0.797
cronbach.alpha(SRPICAA)

Cronbach's alpha for the 'SRPICAA' data-set

Items: 16
Sample units: 92
alpha: 0.752
cronbach.alpha(SRPELSA)

Cronbach's alpha for the 'SRPELSA' data-set

Items: 16
Sample units: 92
alpha: 0.788
cronbach.alpha(SRPASBA)

Cronbach's alpha for the 'SRPASBA' data-set

Items: 16
Sample units: 92
alpha: 0.713
# ICU 

cronbach.alpha(ICUTotA)

Cronbach's alpha for the 'ICUTotA' data-set

Items: 24
Sample units: 92
alpha: 0.802
cronbach.alpha(ICUCalA)

Cronbach's alpha for the 'ICUCalA' data-set

Items: 11
Sample units: 92
alpha: 0.395
cronbach.alpha(ICUUncareA)

Cronbach's alpha for the 'ICUUncareA' data-set

Items: 8
Sample units: 92
alpha: 0.778
cronbach.alpha(ICUUnemoA)

Cronbach's alpha for the 'ICUUnemoA' data-set

Items: 5
Sample units: 92
alpha: 0.888
# LSRP


cronbach.alpha(LevTotA)

Cronbach's alpha for the 'LevTotA' data-set

Items: 24
Sample units: 92
alpha: 0.827
cronbach.alpha(LevPrimA)

Cronbach's alpha for the 'LevPrimA' data-set

Items: 15
Sample units: 92
alpha: 0.804
cronbach.alpha(LevSecA)

Cronbach's alpha for the 'LevSecA' data-set

Items: 9
Sample units: 92
alpha: 0.664
# ZSSS


cronbach.alpha(SSSTotA)

Cronbach's alpha for the 'SSSTotA' data-set

Items: 40
Sample units: 92
alpha: 0.751
cronbach.alpha(SSSDISA)

Cronbach's alpha for the 'SSSDISA' data-set

Items: 10
Sample units: 92
alpha: 0.674
cronbach.alpha(SSSBorA)

Cronbach's alpha for the 'SSSBorA' data-set

Items: 10
Sample units: 92
alpha: 0.483
cronbach.alpha(SSSThrilA)

Cronbach's alpha for the 'SSSThrilA' data-set

Items: 10
Sample units: 92
alpha: 0.8
cronbach.alpha(SSSExpA)

Cronbach's alpha for the 'SSSExpA' data-set

Items: 10
Sample units: 92
alpha: 0.425

Table 1 (Descriptives)

Survey Means

# full 

FSDescriptives <- FSFSurveyT1 |> 
   summarise(
     across(
       .cols = c(SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal, ICUTotScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore, LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal,SSSThrilTotal
                 ),
       .fns = c( # this is used to describe the function within a list of the output (i.e., mean and sd), and the "\" is just shorthand for "function"
         mean = \(x) mean(x, na.rm = T),
         sd = \(x) sd(x, na.rm =T),
         min = \(x) min(x, na.rm = T),
         max = \(x) max(x, na.rm = T)
       ),
       .names = '{.col} ---- {.fn}'
     )
   ) |> 
  pivot_longer(
    cols = everything()
  ) 
  

knitr::kable(FSDescriptives)
name value
SRPTotalScore —- mean 141.554348
SRPTotalScore —- sd 22.906098
SRPTotalScore —- min 74.000000
SRPTotalScore —- max 195.000000
SRPIPMTotal —- mean 37.684783
SRPIPMTotal —- sd 7.609897
SRPIPMTotal —- min 18.000000
SRPIPMTotal —- max 57.000000
SRPCATotal —- mean 36.945652
SRPCATotal —- sd 7.564178
SRPCATotal —- min 20.000000
SRPCATotal —- max 57.000000
SRPELSTotal —- mean 42.163044
SRPELSTotal —- sd 8.894099
SRPELSTotal —- min 16.000000
SRPELSTotal —- max 61.000000
SRPASBTotal —- mean 24.760870
SRPASBTotal —- sd 6.947795
SRPASBTotal —- min 16.000000
SRPASBTotal —- max 42.000000
ICUTotScore —- mean 42.717391
ICUTotScore —- sd 7.124214
ICUTotScore —- min 30.000000
ICUTotScore —- max 57.000000
ICUUncareTotalScore —- mean 14.315217
ICUUncareTotalScore —- sd 3.673275
ICUUncareTotalScore —- min 8.000000
ICUUncareTotalScore —- max 24.000000
ICUUnemoTotal —- mean 12.869565
ICUUnemoTotal —- sd 3.911711
ICUUnemoTotal —- min 5.000000
ICUUnemoTotal —- max 20.000000
LevTotalScore —- mean 46.532609
LevTotalScore —- sd 7.609206
LevTotalScore —- min 27.000000
LevTotalScore —- max 61.000000
LevPrimTotalScore —- mean 28.217391
LevPrimTotalScore —- sd 5.232666
LevPrimTotalScore —- min 18.000000
LevPrimTotalScore —- max 38.000000
LevSecTotalScore —- mean 18.315217
LevSecTotalScore —- sd 3.563951
LevSecTotalScore —- min 9.000000
LevSecTotalScore —- max 27.000000
SSSTotalScore —- mean 17.119565
SSSTotalScore —- sd 5.516892
SSSTotalScore —- min 2.000000
SSSTotalScore —- max 29.000000
SSSDISTotal —- mean 3.978261
SSSDISTotal —- sd 2.362613
SSSDISTotal —- min 0.000000
SSSDISTotal —- max 9.000000
SSSThrilTotal —- mean 6.086957
SSSThrilTotal —- sd 2.884792
SSSThrilTotal —- min 0.000000
SSSThrilTotal —- max 10.000000
# female 

 
FSDescriptivesFemale <- FSFSurveyT1 |> 
   filter(GenderNumb == "1") |> 
   summarise(
     across(
       .cols = c(SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
                 ICUTotScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
                 LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal,
                 SSSThrilTotal
       ),
       .fns = c( 
         mean = \(x) mean(x, na.rm = T),
         sd = \(x) sd(x, na.rm =T),
         min = \(x) min(x, na.rm = T),
         max = \(x) max(x, na.rm = T)
       ),
       .names = '{.col} ---- {.fn}'
     )
   ) |> 
   pivot_longer(
     cols = everything()
   )
 
knitr::kable(FSDescriptivesFemale)
name value
SRPTotalScore —- mean 138.985916
SRPTotalScore —- sd 23.912634
SRPTotalScore —- min 74.000000
SRPTotalScore —- max 194.000000
SRPIPMTotal —- mean 37.126761
SRPIPMTotal —- sd 8.095730
SRPIPMTotal —- min 18.000000
SRPIPMTotal —- max 57.000000
SRPCATotal —- mean 35.295775
SRPCATotal —- sd 6.776840
SRPCATotal —- min 20.000000
SRPCATotal —- max 54.000000
SRPELSTotal —- mean 41.915493
SRPELSTotal —- sd 9.401742
SRPELSTotal —- min 16.000000
SRPELSTotal —- max 61.000000
SRPASBTotal —- mean 24.647887
SRPASBTotal —- sd 6.911892
SRPASBTotal —- min 16.000000
SRPASBTotal —- max 42.000000
ICUTotScore —- mean 41.661972
ICUTotScore —- sd 7.197110
ICUTotScore —- min 30.000000
ICUTotScore —- max 57.000000
ICUUncareTotalScore —- mean 14.070422
ICUUncareTotalScore —- sd 3.896240
ICUUncareTotalScore —- min 8.000000
ICUUncareTotalScore —- max 24.000000
ICUUnemoTotal —- mean 12.380282
ICUUnemoTotal —- sd 3.822363
ICUUnemoTotal —- min 5.000000
ICUUnemoTotal —- max 20.000000
LevTotalScore —- mean 45.788732
LevTotalScore —- sd 7.882921
LevTotalScore —- min 27.000000
LevTotalScore —- max 61.000000
LevPrimTotalScore —- mean 27.591549
LevPrimTotalScore —- sd 5.172945
LevPrimTotalScore —- min 18.000000
LevPrimTotalScore —- max 37.000000
LevSecTotalScore —- mean 18.197183
LevSecTotalScore —- sd 3.804583
LevSecTotalScore —- min 9.000000
LevSecTotalScore —- max 27.000000
SSSTotalScore —- mean 16.774648
SSSTotalScore —- sd 5.695104
SSSTotalScore —- min 2.000000
SSSTotalScore —- max 29.000000
SSSDISTotal —- mean 4.000000
SSSDISTotal —- sd 2.420154
SSSDISTotal —- min 0.000000
SSSDISTotal —- max 9.000000
SSSThrilTotal —- mean 5.718310
SSSThrilTotal —- sd 2.889306
SSSThrilTotal —- min 0.000000
SSSThrilTotal —- max 10.000000
# male 
 
FSDescriptivesMale <- FSFSurveyT1 |> 
  filter(GenderNumb == "2") |> 
  summarise(
    across(
      .cols = c(SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
                ICUTotScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
                LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal,
                SSSThrilTotal
      ),
      .fns = c( 
        mean = \(x) mean(x, na.rm = T),
        sd = \(x) sd(x, na.rm =T)
      ),
      .names = '{.col} ---- {.fn}'
    )
  ) |> 
  pivot_longer(
    cols = everything()
  )

knitr::kable(FSDescriptivesMale)
name value
SRPTotalScore —- mean 150.238095
SRPTotalScore —- sd 16.834205
SRPIPMTotal —- mean 39.571429
SRPIPMTotal —- sd 5.408987
SRPCATotal —- mean 42.523809
SRPCATotal —- sd 7.567160
SRPELSTotal —- mean 43.000000
SRPELSTotal —- sd 7.042727
SRPASBTotal —- mean 25.142857
SRPASBTotal —- sd 7.226934
ICUTotScore —- mean 46.285714
ICUTotScore —- sd 5.684566
ICUUncareTotalScore —- mean 15.142857
ICUUncareTotalScore —- sd 2.707133
ICUUnemoTotal —- mean 14.523810
ICUUnemoTotal —- sd 3.842122
LevTotalScore —- mean 49.047619
LevTotalScore —- sd 6.111270
LevPrimTotalScore —- mean 30.333333
LevPrimTotalScore —- sd 4.983306
LevSecTotalScore —- mean 18.714286
LevSecTotalScore —- sd 2.629503
SSSTotalScore —- mean 18.285714
SSSTotalScore —- sd 4.807732
SSSDISTotal —- mean 3.904762
SSSDISTotal —- sd 2.211442
SSSThrilTotal —- mean 7.333333
SSSThrilTotal —- sd 2.556039

ANS Means

As mentioned in the manuscript, some individuals SC that exceeded the maximum threshold of 9.99 of the NeuLog instrument. Therefore, there is sample number variation between HR, SC. Additionally, there are two tasks present which subdivided the sample further.

# Full 

## Baseline 

stat.desc(FSFHRT1$HRbaseline)
     nbr.val     nbr.null       nbr.na          min          max        range 
  92.0000000    0.0000000    0.0000000   43.5285714  118.4214286   74.8928571 
         sum       median         mean      SE.mean CI.mean.0.95          var 
6637.2500000   68.6571429   72.1440217    1.4811676    2.9421576  201.8348960 
     std.dev     coef.var 
  14.2068609    0.1969236 
stat.desc(FSFSCT1$SCbaseline)
     nbr.val     nbr.null       nbr.na          min          max        range 
  89.0000000    0.0000000    0.0000000    0.1522171    4.8776707    4.7254536 
         sum       median         mean      SE.mean CI.mean.0.95          var 
 146.1194671    1.3072307    1.6417918    0.1228692    0.2441768    1.3436196 
     std.dev     coef.var 
   1.1591461    0.7060250 
## Social Stressor 

stat.desc(SSFHRT1$SSHRCombAUCi)
       nbr.val       nbr.null         nbr.na            min            max 
     43.000000       0.000000       0.000000   -4320.742857    4673.521429 
         range            sum         median           mean        SE.mean 
   8994.264286   22290.600000     500.842857     518.386047     317.340451 
  CI.mean.0.95            var        std.dev       coef.var 
    640.418957 4330313.347240    2080.940496       4.014268 
stat.desc(SSFSCT1$SSSCCombAUCi)
      nbr.val      nbr.null        nbr.na           min           max 
   41.0000000     0.0000000     0.0000000    -0.6968471  1058.9069914 
        range           sum        median          mean       SE.mean 
 1059.6038386 16824.2078307   373.5585014   410.3465325    40.1657903 
 CI.mean.0.95           var       std.dev      coef.var 
   81.1780903 66144.9190668   257.1865453     0.6267545 
## Countdown 

stat.desc(CDFHRT1$CDHeartSignal12)
     nbr.val     nbr.null       nbr.na          min          max        range 
  49.0000000    1.0000000    0.0000000  -19.8000000   14.8083333   34.6083333 
         sum       median         mean      SE.mean CI.mean.0.95          var 
  -5.0166667   -0.2166667   -0.1023810    0.8030556    1.6146515   31.6000174 
     std.dev     coef.var 
   5.6213893  -54.9065929 
stat.desc(CDFHRT1$CDHeartUnsig12)
     nbr.val     nbr.null       nbr.na          min          max        range 
  49.0000000    1.0000000    0.0000000  -11.7333333   11.5500000   23.2833333 
         sum       median         mean      SE.mean CI.mean.0.95          var 
 -17.7500000   -0.6000000   -0.3622449    0.5812107    1.1686024   16.5524858 
     std.dev     coef.var 
   4.0684746  -11.2312821 
stat.desc(CDFHRT1$HrSigRecovMean)
     nbr.val     nbr.null       nbr.na          min          max        range 
  49.0000000    1.0000000    0.0000000  -23.0833333    9.5416667   32.6250000 
         sum       median         mean      SE.mean CI.mean.0.95          var 
 -83.7750000   -0.1250000   -1.7096939    0.8933322    1.7961648   39.1040809 
     std.dev     coef.var 
   6.2533256   -3.6575703 
stat.desc(CDFHRT1$HrUnSigRecovMean)
     nbr.val     nbr.null       nbr.na          min          max        range 
  49.0000000    1.0000000    0.0000000  -11.5750000    9.4750000   21.0500000 
         sum       median         mean      SE.mean CI.mean.0.95          var 
 -49.2916667   -0.8666667   -1.0059524    0.7027512    1.4129759   24.1991001 
     std.dev     coef.var 
   4.9192581   -4.8901500 
stat.desc(CDFSCT1$CDSkinSignal12)
     nbr.val     nbr.null       nbr.na          min          max        range 
 48.00000000   0.00000000   0.00000000  -0.04290417   0.49067917   0.53358333 
         sum       median         mean      SE.mean CI.mean.0.95          var 
  3.95463667   0.04147292   0.08238826   0.01447807   0.02912613   0.01006150 
     std.dev     coef.var 
  0.10030704   1.21749186 
stat.desc(CDFSCT1$CDSkinUnsig12)
      nbr.val      nbr.null        nbr.na           min           max 
 48.000000000   0.000000000   0.000000000  -0.218163333   0.305742500 
        range           sum        median          mean       SE.mean 
  0.523905833  -0.332931667  -0.004957083  -0.006936076   0.011432141 
 CI.mean.0.95           var       std.dev      coef.var 
  0.022998500   0.006273304   0.079204193 -11.419163920 
stat.desc(CDFSCT1$ScSigRecovMean)
     nbr.val     nbr.null       nbr.na          min          max        range 
 48.00000000   0.00000000   0.00000000  -0.05321750   1.46255250   1.51577000 
         sum       median         mean      SE.mean CI.mean.0.95          var 
 11.07441500   0.12455458   0.23071698   0.04656247   0.09367161   0.10406705 
     std.dev     coef.var 
  0.32259426   1.39822503 
stat.desc(CDFSCT1$ScUnSigRecovMean)
     nbr.val     nbr.null       nbr.na          min          max        range 
 48.00000000   0.00000000   0.00000000  -0.10906250   0.94537500   1.05443750 
         sum       median         mean      SE.mean CI.mean.0.95          var 
  9.32657500   0.14871833   0.19430365   0.03158500   0.06354083   0.04788540 
     std.dev     coef.var 
  0.21882732   1.12621314 
# Male 

## Baseline 

stat.desc(MHRbT1$HRbaseline)
     nbr.val     nbr.null       nbr.na          min          max        range 
  21.0000000    0.0000000    0.0000000   43.5285714   96.2500000   52.7214286 
         sum       median         mean      SE.mean CI.mean.0.95          var 
1382.1714286   65.5000000   65.8176871    2.7980315    5.8365915  164.4085899 
     std.dev     coef.var 
  12.8221913    0.1948138 
stat.desc(MSCbT1$SCbaseline)
     nbr.val     nbr.null       nbr.na          min          max        range 
  20.0000000    0.0000000    0.0000000    0.3198686    4.8776707    4.5578021 
         sum       median         mean      SE.mean CI.mean.0.95          var 
  38.9497236    1.5946993    1.9474862    0.2736521    0.5727604    1.4977091 
     std.dev     coef.var 
   1.2238092    0.6284046 
## Social Stressor 

stat.desc(MSSHRT1$SSHRCombAUCi)
      nbr.val      nbr.null        nbr.na           min           max 
     10.00000       0.00000       0.00000   -2724.00000    4673.52143 
        range           sum        median          mean       SE.mean 
   7397.52143    4609.68571     294.56071     460.96857     748.57964 
 CI.mean.0.95           var       std.dev      coef.var 
   1693.40480 5603714.82271    2367.21668       5.13531 
stat.desc(MSSSCT1$SSSCCombAUCi)
      nbr.val      nbr.null        nbr.na           min           max 
    9.0000000     0.0000000     0.0000000   123.5120343  1058.9069914 
        range           sum        median          mean       SE.mean 
  935.3949571  4066.5755379   418.2931671   451.8417264    99.4922953 
 CI.mean.0.95           var       std.dev      coef.var 
  229.4296444 89088.4514317   298.4768859     0.6605784 
## Countdown 

stat.desc(MCDHRT1$CDHeartSignal12)
     nbr.val     nbr.null       nbr.na          min          max        range 
   11.000000     0.000000     0.000000   -19.800000     6.075000    25.875000 
         sum       median         mean      SE.mean CI.mean.0.95          var 
  -55.783333    -4.666667    -5.071212     2.024427     4.510704    45.081352 
     std.dev     coef.var 
    6.714265    -1.323996 
stat.desc(MCDHRT1$CDHeartUnsig12)
     nbr.val     nbr.null       nbr.na          min          max        range 
   11.000000     0.000000     0.000000   -11.733333     3.208333    14.941667 
         sum       median         mean      SE.mean CI.mean.0.95          var 
  -20.416667    -1.416667    -1.856061     1.213596     2.704061    16.200973 
     std.dev     coef.var 
    4.025043    -2.168595 
stat.desc(MCDHRT1$HrSigRecovMean)
     nbr.val     nbr.null       nbr.na          min          max        range 
 11.00000000   0.00000000   0.00000000  -5.29166667   6.07500000  11.36666667 
         sum       median         mean      SE.mean CI.mean.0.95          var 
  0.66666667   0.66666667   0.06060606   1.27778370   2.84707950  17.96004293 
     std.dev     coef.var 
  4.23792908  69.92582990 
stat.desc(MCDHRT1$HrUnSigRecovMean)
     nbr.val     nbr.null       nbr.na          min          max        range 
  11.0000000    0.0000000    0.0000000   -9.0250000    9.4000000   18.4250000 
         sum       median         mean      SE.mean CI.mean.0.95          var 
   4.4166667    0.5333333    0.4015152    1.6214210    3.6127511   28.9190669 
     std.dev     coef.var 
   5.3776451   13.3933803 
stat.desc(MCDSCT1$CDSkinSignal12)
     nbr.val     nbr.null       nbr.na          min          max        range 
 11.00000000   0.00000000   0.00000000  -0.04290417   0.19242833   0.23533250 
         sum       median         mean      SE.mean CI.mean.0.95          var 
  0.56964583   0.03981917   0.05178598   0.01889151   0.04209290   0.00392578 
     std.dev     coef.var 
  0.06265605   1.20990354 
stat.desc(MCDSCT1$CDSkinUnsig12)
      nbr.val      nbr.null        nbr.na           min           max 
11.0000000000  0.0000000000  0.0000000000 -0.1759658333  0.1112358333 
        range           sum        median          mean       SE.mean 
 0.2872016667  0.0255025000  0.0007483333  0.0023184091  0.0265923046 
 CI.mean.0.95           var       std.dev      coef.var 
 0.0592513470  0.0077786573  0.0881966967 38.0419042627 
stat.desc(MCDSCT1$ScSigRecovMean)
     nbr.val     nbr.null       nbr.na          min          max        range 
 11.00000000   0.00000000   0.00000000  -0.02178917   1.04046583   1.06225500 
         sum       median         mean      SE.mean CI.mean.0.95          var 
  2.16133917   0.09930583   0.19648538   0.08808900   0.19627452   0.08535639 
     std.dev     coef.var 
  0.29215815   1.48692057 
stat.desc(MCDSCT1$ScUnSigRecovMean)
     nbr.val     nbr.null       nbr.na          min          max        range 
 11.00000000   0.00000000   0.00000000   0.00062750   0.75934333   0.75871583 
         sum       median         mean      SE.mean CI.mean.0.95          var 
  1.94321417   0.15610000   0.17665583   0.06429570   0.14325974   0.04547331 
     std.dev     coef.var 
  0.21324471   1.20711954 
# Female 

## Baseline 

stat.desc(FemaleHRbaseT1$HRbaseline)
     nbr.val     nbr.null       nbr.na          min          max        range 
  71.0000000    0.0000000    0.0000000   49.6000000  118.4214286   68.8214286 
         sum       median         mean      SE.mean CI.mean.0.95          var 
5255.0785714   72.6500000   74.0151911    1.6777476    3.3461621  199.8534358 
     std.dev     coef.var 
  14.1369528    0.1910007 
stat.desc(FemaleSCbaseT1$SCbaseline)
     nbr.val     nbr.null       nbr.na          min          max        range 
  69.0000000    0.0000000    0.0000000    0.1522171    4.7345321    4.5823150 
         sum       median         mean      SE.mean CI.mean.0.95          var 
 107.1697436    1.1533514    1.5531847    0.1364600    0.2723018    1.2848726 
     std.dev     coef.var 
   1.1335222    0.7298052 
## Social Stressor 

stat.desc(FemaleSSHRT1$SSHRCombAUCi)
       nbr.val       nbr.null         nbr.na            min            max 
     33.000000       0.000000       0.000000   -4320.742857    4346.685714 
         range            sum         median           mean        SE.mean 
   8667.428571   17680.914286     500.842857     535.785281     352.744608 
  CI.mean.0.95            var        std.dev       coef.var 
    718.517255 4106149.041058    2026.363502       3.782044 
stat.desc(FemaleSCSST1$SSSCCombAUCi)
      nbr.val      nbr.null        nbr.na           min           max 
   32.0000000     0.0000000     0.0000000    -0.6968471   914.7494050 
        range           sum        median          mean       SE.mean 
  915.4462521 12757.6322929   364.8510800   398.6760092    43.9165500 
 CI.mean.0.95           var       std.dev      coef.var 
   89.5683942 61717.2275955   248.4295224     0.6231364 
## Countdown 

stat.desc(FemaleHRCDT1$CDHeartSignal12)
     nbr.val     nbr.null       nbr.na          min          max        range 
 38.00000000   1.00000000   0.00000000  -5.57500000  14.80833333  20.38333333 
         sum       median         mean      SE.mean CI.mean.0.95          var 
 50.76666667  -0.07083333   1.33596491   0.71350981   1.44570819  19.34565730 
     std.dev     coef.var 
  4.39836985   3.29227946 
stat.desc(FemaleHRCDT1$CDHeartUnsig12)
     nbr.val     nbr.null       nbr.na          min          max        range 
 38.00000000   1.00000000   0.00000000  -9.58333333  11.55000000  21.13333333 
         sum       median         mean      SE.mean CI.mean.0.95          var 
  2.66666667  -0.17916667   0.07017544   0.65372210   1.32456678  16.23939802 
     std.dev     coef.var 
  4.02981365  57.42484445 
stat.desc(FemaleHRCDT1$HrSigRecovMean)
     nbr.val     nbr.null       nbr.na          min          max        range 
   38.000000     1.000000     0.000000   -23.083333     9.541667    32.625000 
         sum       median         mean      SE.mean CI.mean.0.95          var 
  -84.441667    -0.650000    -2.222149     1.084267     2.196934    44.674126 
     std.dev     coef.var 
    6.683871    -3.007841 
stat.desc(FemaleHRCDT1$HrUnSigRecovMean)
     nbr.val     nbr.null       nbr.na          min          max        range 
  38.0000000    1.0000000    0.0000000  -11.5750000    9.4750000   21.0500000 
         sum       median         mean      SE.mean CI.mean.0.95          var 
 -53.7083333   -1.1291667   -1.4133772    0.7749032    1.5701029   22.8180463 
     std.dev     coef.var 
   4.7768239   -3.3797233 
stat.desc(FemaleSCCDT1$CDSkinSignal12)
     nbr.val     nbr.null       nbr.na          min          max        range 
 37.00000000   0.00000000   0.00000000  -0.01934167   0.49067917   0.51002083 
         sum       median         mean      SE.mean CI.mean.0.95          var 
  3.38499083   0.08083167   0.09148624   0.01776280   0.03602463   0.01167413 
     std.dev     coef.var 
  0.10804689   1.18101797 
stat.desc(FemaleSCCDT1$CDSkinUnsig12)
     nbr.val     nbr.null       nbr.na          min          max        range 
37.000000000  0.000000000  0.000000000 -0.218163333  0.305742500  0.523905833 
         sum       median         mean      SE.mean CI.mean.0.95          var 
-0.358434167 -0.007892500 -0.009687410  0.012729472  0.025816565  0.005995459 
     std.dev     coef.var 
 0.077430352 -7.992884904 
stat.desc(FemaleSCCDT1$ScSigRecovMean)
     nbr.val     nbr.null       nbr.na          min          max        range 
 37.00000000   0.00000000   0.00000000  -0.05321750   1.46255250   1.51577000 
         sum       median         mean      SE.mean CI.mean.0.95          var 
  8.91307583   0.13283500   0.24089394   0.05494241   0.11142836   0.11169071 
     std.dev     coef.var 
  0.33420161   1.38733920 
stat.desc(FemaleSCCDT1$ScUnSigRecovMean)
     nbr.val     nbr.null       nbr.na          min          max        range 
 37.00000000   0.00000000   0.00000000  -0.10906250   0.94537500   1.05443750 
         sum       median         mean      SE.mean CI.mean.0.95          var 
  7.38336083   0.14172083   0.19955029   0.03667318   0.07437665   0.04976211 
     std.dev     coef.var 
  0.22307424   1.11788479 

t-tests

ANS

# baseline 

ind.t.test1<- t.test(HRbaseline ~ Gender, data = FSFHRT1)
ind.t.test1

    Welch Two Sample t-test

data:  HRbaseline by Gender
t = 2.5127, df = 35.65, p-value = 0.01665
alternative hypothesis: true difference in means between group Female and group Male   is not equal to 0
95 percent confidence interval:
  1.578621 14.816388
sample estimates:
mean in group Female mean in group Male   
            74.01519             65.81769 
ind.t.test1<- t.test(SCbaseline ~ Gender, data = FSFSCT1)
ind.t.test1

    Welch Two Sample t-test

data:  SCbaseline by Gender
t = -1.2895, df = 29.121, p-value = 0.2074
alternative hypothesis: true difference in means between group Female and group Male   is not equal to 0
95 percent confidence interval:
 -1.019597  0.230994
sample estimates:
mean in group Female mean in group Male   
            1.553185             1.947486 
#SS
ind.t.test1<- t.test(SSHRCombAUCi ~ Gender, data = SSFHRT1)
ind.t.test1

    Welch Two Sample t-test

data:  SSHRCombAUCi by Gender
t = 0.09041, df = 13.257, p-value = 0.9293
alternative hypothesis: true difference in means between group Female and group Male   is not equal to 0
95 percent confidence interval:
 -1709.432  1859.066
sample estimates:
mean in group Female mean in group Male   
            535.7853             460.9686 
ind.t.test1<- t.test(SSSCCombAUCi ~ Gender, data = SSFSCT1)
ind.t.test1

    Welch Two Sample t-test

data:  SSSCCombAUCi by Gender
t = -0.48886, df = 11.31, p-value = 0.6343
alternative hypothesis: true difference in means between group Female and group Male   is not equal to 0
95 percent confidence interval:
 -291.7323  185.4008
sample estimates:
mean in group Female mean in group Male   
            398.6760             451.8417 
# CD

ind.t.test1<- t.test(CDHeartSignal12 ~ Gender, data = CDFHRT1)
ind.t.test1

    Welch Two Sample t-test

data:  CDHeartSignal12 by Gender
t = 2.985, df = 12.586, p-value = 0.01087
alternative hypothesis: true difference in means between group Female and group Male   is not equal to 0
95 percent confidence interval:
  1.754432 11.059922
sample estimates:
mean in group Female mean in group Male   
            1.335965            -5.071212 
ind.t.test1<- t.test(CDHeartUnsig12 ~ Gender, data = CDFHRT1)
ind.t.test1

    Welch Two Sample t-test

data:  CDHeartUnsig12 by Gender
t = 1.3974, df = 16.275, p-value = 0.1811
alternative hypothesis: true difference in means between group Female and group Male   is not equal to 0
95 percent confidence interval:
 -0.9919762  4.8444482
sample estimates:
mean in group Female mean in group Male   
          0.07017544          -1.85606061 
ind.t.test1<- t.test(HrSigRecovMean ~ Gender, data = CDFHRT1)
ind.t.test1

    Welch Two Sample t-test

data:  HrSigRecovMean by Gender
t = -1.3622, df = 25.949, p-value = 0.1849
alternative hypothesis: true difference in means between group Female and group Male   is not equal to 0
95 percent confidence interval:
 -5.727776  1.162266
sample estimates:
mean in group Female mean in group Male   
         -2.22214912           0.06060606 
ind.t.test1<- t.test(HrUnSigRecovMean ~ Gender, data = CDFHRT1)
ind.t.test1

    Welch Two Sample t-test

data:  HrUnSigRecovMean by Gender
t = -1.0099, df = 14.88, p-value = 0.3287
alternative hypothesis: true difference in means between group Female and group Male   is not equal to 0
95 percent confidence interval:
 -5.647962  2.018177
sample estimates:
mean in group Female mean in group Male   
          -1.4133772            0.4015152 
ind.t.test1<- t.test(CDSkinSignal12 ~ Gender, data = CDFSCT1)
ind.t.test1

    Welch Two Sample t-test

data:  CDSkinSignal12 by Gender
t = 1.531, df = 29.165, p-value = 0.1365
alternative hypothesis: true difference in means between group Female and group Male   is not equal to 0
95 percent confidence interval:
 -0.01332112  0.09272163
sample estimates:
mean in group Female mean in group Male   
          0.09148624           0.05178598 
ind.t.test1<- t.test(CDSkinUnsig12 ~ Gender, data = CDFSCT1)
ind.t.test1

    Welch Two Sample t-test

data:  CDSkinUnsig12 by Gender
t = -0.40722, df = 14.891, p-value = 0.6896
alternative hypothesis: true difference in means between group Female and group Male   is not equal to 0
95 percent confidence interval:
 -0.07488545  0.05087381
sample estimates:
mean in group Female mean in group Male   
        -0.009687410          0.002318409 
ind.t.test1<- t.test(ScSigRecovMean ~ Gender, data = CDFSCT1)
ind.t.test1

    Welch Two Sample t-test

data:  ScSigRecovMean by Gender
t = 0.42775, df = 18.515, p-value = 0.6738
alternative hypothesis: true difference in means between group Female and group Male   is not equal to 0
95 percent confidence interval:
 -0.1732722  0.2620893
sample estimates:
mean in group Female mean in group Male   
           0.2408939            0.1964854 
ind.t.test1<- t.test(ScUnSigRecovMean ~ Gender, data = CDFSCT1)
ind.t.test1

    Welch Two Sample t-test

data:  ScUnSigRecovMean by Gender
t = 0.3093, df = 17.063, p-value = 0.7608
alternative hypothesis: true difference in means between group Female and group Male   is not equal to 0
95 percent confidence interval:
 -0.1332284  0.1790173
sample estimates:
mean in group Female mean in group Male   
           0.1995503            0.1766558 

Survey

# SRP 


ind.t.test1<- t.test(SRPTotalScore ~ Gender, data = FSFSurveyT1)
ind.t.test1

    Welch Two Sample t-test

data:  SRPTotalScore by Gender
t = -2.424, df = 46.285, p-value = 0.01932
alternative hypothesis: true difference in means between group Female and group Male   is not equal to 0
95 percent confidence interval:
 -20.594560  -1.909799
sample estimates:
mean in group Female mean in group Male   
            138.9859             150.2381 
ind.t.test1<- t.test(SRPIPMTotal ~ Gender, data = FSFSurveyT1)
ind.t.test1

    Welch Two Sample t-test

data:  SRPIPMTotal by Gender
t = -1.6063, df = 49.122, p-value = 0.1146
alternative hypothesis: true difference in means between group Female and group Male   is not equal to 0
95 percent confidence interval:
 -5.5029328  0.6135968
sample estimates:
mean in group Female mean in group Male   
            37.12676             39.57143 
ind.t.test1<- t.test(SRPCATotal ~ Gender, data = FSFSurveyT1)
ind.t.test1

    Welch Two Sample t-test

data:  SRPCATotal by Gender
t = -3.9353, df = 30.13, p-value = 0.0004535
alternative hypothesis: true difference in means between group Female and group Male   is not equal to 0
95 percent confidence interval:
 -10.978471  -3.477599
sample estimates:
mean in group Female mean in group Male   
            35.29577             42.52381 
ind.t.test1<- t.test(SRPELSTotal ~ Gender, data = FSFSurveyT1)
ind.t.test1

    Welch Two Sample t-test

data:  SRPELSTotal by Gender
t = -0.57104, df = 43.211, p-value = 0.5709
alternative hypothesis: true difference in means between group Female and group Male   is not equal to 0
95 percent confidence interval:
 -4.914022  2.745008
sample estimates:
mean in group Female mean in group Male   
            41.91549             43.00000 
ind.t.test1<- t.test(SRPASBTotal ~ Gender, data = FSFSurveyT1)
ind.t.test1

    Welch Two Sample t-test

data:  SRPASBTotal by Gender
t = -0.27844, df = 31.625, p-value = 0.7825
alternative hypothesis: true difference in means between group Female and group Male   is not equal to 0
95 percent confidence interval:
 -4.117561  3.127621
sample estimates:
mean in group Female mean in group Male   
            24.64789             25.14286 
# ICU 


ind.t.test1<- t.test(ICUTotScore ~ Gender, data = FSFSurveyT1)
ind.t.test1

    Welch Two Sample t-test

data:  ICUTotScore by Gender
t = -3.07, df = 40.837, p-value = 0.003796
alternative hypothesis: true difference in means between group Female and group Male   is not equal to 0
95 percent confidence interval:
 -7.665736 -1.581749
sample estimates:
mean in group Female mean in group Male   
            41.66197             46.28571 
ind.t.test1<- t.test(ICUUncareTotalScore ~ Gender, data = FSFSurveyT1)
ind.t.test1

    Welch Two Sample t-test

data:  ICUUncareTotalScore by Gender
t = -1.4295, df = 46.976, p-value = 0.1595
alternative hypothesis: true difference in means between group Female and group Male   is not equal to 0
95 percent confidence interval:
 -2.5816516  0.4367824
sample estimates:
mean in group Female mean in group Male   
            14.07042             15.14286 
ind.t.test1<- t.test(ICUUnemoTotal ~ Gender, data = FSFSurveyT1)
ind.t.test1

    Welch Two Sample t-test

data:  ICUUnemoTotal by Gender
t = -2.2486, df = 32.625, p-value = 0.03142
alternative hypothesis: true difference in means between group Female and group Male   is not equal to 0
95 percent confidence interval:
 -4.0838237 -0.2032319
sample estimates:
mean in group Female mean in group Male   
            12.38028             14.52381 
# LSRP

ind.t.test1<- t.test(LevTotalScore ~ Gender, data = FSFSurveyT1)
ind.t.test1

    Welch Two Sample t-test

data:  LevTotalScore by Gender
t = -2.0005, df = 41.647, p-value = 0.05199
alternative hypothesis: true difference in means between group Female and group Male   is not equal to 0
95 percent confidence interval:
 -6.54718941  0.02941611
sample estimates:
mean in group Female mean in group Male   
            45.78873             49.04762 
ind.t.test1<- t.test(LevPrimTotalScore ~ Gender, data = FSFSurveyT1)
ind.t.test1

    Welch Two Sample t-test

data:  LevPrimTotalScore by Gender
t = -2.1956, df = 33.799, p-value = 0.03509
alternative hypothesis: true difference in means between group Female and group Male   is not equal to 0
95 percent confidence interval:
 -5.2801499 -0.2034181
sample estimates:
mean in group Female mean in group Male   
            27.59155             30.33333 
ind.t.test1<- t.test(LevSecTotalScore ~ Gender, data = FSFSurveyT1)
ind.t.test1

    Welch Two Sample t-test

data:  LevSecTotalScore by Gender
t = -0.70821, df = 47.259, p-value = 0.4823
alternative hypothesis: true difference in means between group Female and group Male   is not equal to 0
95 percent confidence interval:
 -1.9857677  0.9515625
sample estimates:
mean in group Female mean in group Male   
            18.19718             18.71429 
# SSS

ind.t.test1<- t.test(SSSTotalScore ~ Gender, data = FSFSurveyT1)
ind.t.test1

    Welch Two Sample t-test

data:  SSSTotalScore by Gender
t = -1.2108, df = 38.168, p-value = 0.2334
alternative hypothesis: true difference in means between group Female and group Male   is not equal to 0
95 percent confidence interval:
 -4.037142  1.015009
sample estimates:
mean in group Female mean in group Male   
            16.77465             18.28571 
ind.t.test1<- t.test(SSSDISTotal ~ Gender, data = FSFSurveyT1)
ind.t.test1

    Welch Two Sample t-test

data:  SSSDISTotal by Gender
t = 0.16959, df = 35.41, p-value = 0.8663
alternative hypothesis: true difference in means between group Female and group Male   is not equal to 0
95 percent confidence interval:
 -1.044363  1.234840
sample estimates:
mean in group Female mean in group Male   
            4.000000             3.904762 
ind.t.test1<- t.test(SSSThrilTotal ~ Gender, data = FSFSurveyT1)
ind.t.test1

    Welch Two Sample t-test

data:  SSSThrilTotal by Gender
t = -2.4666, df = 36.485, p-value = 0.01846
alternative hypothesis: true difference in means between group Female and group Male   is not equal to 0
95 percent confidence interval:
 -2.9422934 -0.2877535
sample estimates:
mean in group Female mean in group Male   
            5.718310             7.333333 

Distributions of DVs

# Histogram function 

histo <- function(df, var, title = "Histogram", xlab = "DV", ylab = "Frequency", col = "honeydew", border = "black", bins = 5){
  df |> 
  ggplot(aes(x = {{var}})) +
    geom_histogram(binwidth = bins, fill = col, color = border) +
    labs(title = title, x = xlab, y = ylab)
}

SRP Full

Normal = SRPTot, SRPIPMTotal, SRPCATotal, SRPELSTotal
Non-Normal = SRPASBTotal

# SRPTot 

FSFSurveyT1 |> 
  histo(SRPTotalScore)

qqnorm(FSFSurveyT1$SRPTotalScore)
qqline(FSFSurveyT1$SRPTotalScore)

shapiro.test(FSFSurveyT1$SRPTotalScore)

    Shapiro-Wilk normality test

data:  FSFSurveyT1$SRPTotalScore
W = 0.97854, p-value = 0.1335
# SRP IPM 

FSFSurveyT1 |> 
  histo(SRPIPMTotal)

qqnorm(FSFSurveyT1$SRPIPMTotal)
qqline(FSFSurveyT1$SRPIPMTotal)

shapiro.test(FSFSurveyT1$SRPIPMTotal)

    Shapiro-Wilk normality test

data:  FSFSurveyT1$SRPIPMTotal
W = 0.99234, p-value = 0.8779
# SRPCATotal

FSFSurveyT1 |> 
  histo(SRPCATotal)

qqnorm(FSFSurveyT1$SRPCATotal)
qqline(FSFSurveyT1$SRPCATotal)

shapiro.test(FSFSurveyT1$SRPCATotal)

    Shapiro-Wilk normality test

data:  FSFSurveyT1$SRPCATotal
W = 0.98428, p-value = 0.3365
# SRPELSTotal

FSFSurveyT1 |> 
  histo(SRPELSTotal)

qqnorm(FSFSurveyT1$SRPELSTotal)
qqline(FSFSurveyT1$SRPELSTotal)

shapiro.test(FSFSurveyT1$SRPELSTotal)

    Shapiro-Wilk normality test

data:  FSFSurveyT1$SRPELSTotal
W = 0.97861, p-value = 0.1352
# SRPASBTotal


FSFSurveyT1 |> 
  histo(SRPASBTotal)

qqnorm(FSFSurveyT1$SRPASBTotal)
qqline(FSFSurveyT1$SRPASBTotal)

shapiro.test(FSFSurveyT1$SRPASBTotal)

    Shapiro-Wilk normality test

data:  FSFSurveyT1$SRPASBTotal
W = 0.93314, p-value = 0.0001476

ICU Full

Non-Normal = ICUTotScore, ICUUncareTotalScore, ICUUnemoTotal

# ICUtotal 

FSFSurveyT1 |> 
  histo(ICUTotScore)

qqnorm(FSFSurveyT1$ICUTotScore)
qqline(FSFSurveyT1$ICUTotScore)

shapiro.test(FSFSurveyT1$ICUTotScore)

    Shapiro-Wilk normality test

data:  FSFSurveyT1$ICUTotScore
W = 0.96482, p-value = 0.01386
# ICUUncare

FSFSurveyT1 |> 
  histo(ICUUncareTotalScore)

qqnorm(FSFSurveyT1$ICUUncareTotalScore)
qqline(FSFSurveyT1$ICUUncareTotalScore)

shapiro.test(FSFSurveyT1$ICUUncareTotalScore)

    Shapiro-Wilk normality test

data:  FSFSurveyT1$ICUUncareTotalScore
W = 0.96571, p-value = 0.01598
# Unemo 

FSFSurveyT1 |> 
  histo(ICUUnemoTotal)

qqnorm(FSFSurveyT1$ICUUnemoTotal)
qqline(FSFSurveyT1$ICUUnemoTotal)

shapiro.test(FSFSurveyT1$ICUUnemoTotal)

    Shapiro-Wilk normality test

data:  FSFSurveyT1$ICUUnemoTotal
W = 0.97151, p-value = 0.0413

Lev full

Normal = LevTotalScore, LevSecTotalScore
Non-Normal = LevPrimTotalScore

# Leve tot 


FSFSurveyT1 |> 
  histo(LevTotalScore)

qqnorm(FSFSurveyT1$LevTotalScore)
qqline(FSFSurveyT1$LevTotalScore)

shapiro.test(FSFSurveyT1$LevTotalScore)

    Shapiro-Wilk normality test

data:  FSFSurveyT1$LevTotalScore
W = 0.97816, p-value = 0.1254
# lev prim 


FSFSurveyT1 |> 
  histo(LevPrimTotalScore)

qqnorm(FSFSurveyT1$LevPrimTotalScore)
qqline(FSFSurveyT1$LevPrimTotalScore)

shapiro.test(FSFSurveyT1$LevPrimTotalScore)

    Shapiro-Wilk normality test

data:  FSFSurveyT1$LevPrimTotalScore
W = 0.96646, p-value = 0.01804
# lev sec

FSFSurveyT1 |> 
  histo(LevSecTotalScore)

qqnorm(FSFSurveyT1$LevSecTotalScore)
qqline(FSFSurveyT1$LevSecTotalScore)

shapiro.test(FSFSurveyT1$LevSecTotalScore)

    Shapiro-Wilk normality test

data:  FSFSurveyT1$LevSecTotalScore
W = 0.97963, p-value = 0.1598

SSS Full

Normal = SSSTotalScore
Non-Normal = SSSDISTotal, SSSThrilTotal

# SSS total 

FSFSurveyT1 |> 
  histo(SSSTotalScore)

qqnorm(FSFSurveyT1$SSSTotalScore)
qqline(FSFSurveyT1$SSSTotalScore)

shapiro.test(FSFSurveyT1$SSSTotalScore)

    Shapiro-Wilk normality test

data:  FSFSurveyT1$SSSTotalScore
W = 0.97996, p-value = 0.1689
# SSS dis 

FSFSurveyT1 |> 
  histo(SSSDISTotal)

qqnorm(FSFSurveyT1$SSSDISTotal)
qqline(FSFSurveyT1$SSSDISTotal)

shapiro.test(FSFSurveyT1$SSSDISTotal)

    Shapiro-Wilk normality test

data:  FSFSurveyT1$SSSDISTotal
W = 0.95666, p-value = 0.003886
# SSSThrilTotal
FSFSurveyT1 |> 
  histo(SSSThrilTotal)

qqnorm(FSFSurveyT1$SSSThrilTotal)
qqline(FSFSurveyT1$SSSThrilTotal)

shapiro.test(FSFSurveyT1$SSSThrilTotal)

    Shapiro-Wilk normality test

data:  FSFSurveyT1$SSSThrilTotal
W = 0.92751, p-value = 0.00007295

Table 2 (Partial Correlations for Rest and SSST)

This table contain the baseline and SSST measures will partialing the correlations for gender, race, and age.

Full

HR baseline

#SRP

pcor.test(FSFHRT1$SRPTotalScore, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "pearson")
     estimate   p.value  statistic  n gp  Method
1 -0.02466179 0.8185544 -0.2300998 92  3 pearson
pcor.test(FSFHRT1$SRPIPMTotal, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "pearson")
      estimate   p.value   statistic  n gp  Method
1 -0.001882713 0.9860294 -0.01756081 92  3 pearson
pcor.test(FSFHRT1$SRPCATotal, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "pearson")
    estimate   p.value statistic  n gp  Method
1 0.06532416 0.5430514 0.6106074 92  3 pearson
pcor.test(FSFHRT1$SRPELSTotal, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "pearson")
    estimate   p.value  statistic  n gp  Method
1 -0.0644952 0.5481925 -0.6028263 92  3 pearson
pcor.test(FSFHRT1$SRPASBTotal, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "spearman")
      estimate   p.value   statistic  n gp   Method
1 -0.003973727 0.9705183 -0.03706476 92  3 spearman
# ICU

pcor.test(FSFHRT1$ICUTotScore, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 0.07816455 0.4665563 0.7313079 92  3 spearman
pcor.test(FSFHRT1$ICUUncareTotalScore, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 0.05647354 0.5991254 0.5275921 92  3 spearman
pcor.test(FSFHRT1$ICUUnemoTotal, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 0.03214773 0.7648858 0.3000091 92  3 spearman
# Lev

pcor.test(FSFHRT1$LevTotalScore, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "pearson")
    estimate   p.value statistic  n gp  Method
1 0.03376021 0.7534601 0.3150738 92  3 pearson
pcor.test(FSFHRT1$LevPrimTotalScore, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "spearman")
    estimate  p.value  statistic  n gp   Method
1 0.01071867 0.920588 0.09998288 92  3 spearman
pcor.test(FSFHRT1$LevSecTotalScore, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "pearson")
     estimate   p.value  statistic  n gp  Method
1 -0.02581214 0.8102459 -0.2408399 92  3 pearson
# SSS 

pcor.test(FSFHRT1$SSSTotalScore, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "pearson")
     estimate   p.value statistic  n gp  Method
1 -0.08032739 0.4542764 -0.751673 92  3 pearson
pcor.test(FSFHRT1$SSSDISTotal, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "spearman")
  estimate   p.value statistic  n gp   Method
1 0.067002 0.5327185 0.6263606 92  3 spearman
pcor.test(FSFHRT1$SSSThrilTotal, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "spearman")
    estimate  p.value statistic  n gp   Method
1 -0.1322897 0.216527 -1.244858 92  3 spearman

SC baseline

#SRP

pcor.test(FSFSCT1$SRPTotalScore, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age),method = "pearson")
   estimate   p.value statistic  n gp  Method
1 -0.174352 0.1083755 -1.622819 89  3 pearson
pcor.test(FSFSCT1$SRPIPMTotal, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "pearson")
      estimate   p.value   statistic  n gp  Method
1 -0.002700979 0.9803091 -0.02475497 89  3 pearson
pcor.test(FSFSCT1$SRPCATotal, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "pearson")
    estimate   p.value statistic  n gp  Method
1 -0.1355034 0.2135123  -1.25347 89  3 pearson
pcor.test(FSFSCT1$SRPELSTotal, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "pearson")
    estimate     p.value statistic  n gp  Method
1 -0.3132829 0.003313279 -3.023489 89  3 pearson
pcor.test(FSFSCT1$SRPASBTotal, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "spearman")
      estimate   p.value   statistic  n gp   Method
1 -0.001921123 0.9859938 -0.01760741 89  3 spearman
# ICU

pcor.test(FSFSCT1$ICUTotScore, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "spearman")
      estimate   p.value   statistic  n gp   Method
1 -0.008222469 0.9401053 -0.07536272 89  3 spearman
pcor.test(FSFSCT1$ICUUncareTotalScore, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "spearman")
      estimate   p.value   statistic  n gp   Method
1 -0.006848178 0.9501019 -0.06276606 89  3 spearman
pcor.test(FSFSCT1$ICUUnemoTotal, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "spearman")
   estimate   p.value statistic  n gp   Method
1 0.1357248 0.2127582  1.255556 89  3 spearman
# Lev

pcor.test(FSFSCT1$LevTotalScore, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "pearson")
    estimate   p.value statistic  n gp  Method
1 -0.1115422 0.3065639 -1.028721 89  3 pearson
pcor.test(FSFSCT1$LevPrimTotalScore, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "spearman")
     estimate   p.value  statistic  n gp   Method
1 -0.08616251 0.4302205 -0.7926402 89  3 spearman
pcor.test(FSFSCT1$LevSecTotalScore, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "pearson")
    estimate   p.value statistic  n gp  Method
1 -0.1560232 0.1514214 -1.447706 89  3 pearson
# SSS 

pcor.test(FSFSCT1$SSSTotalScore, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "pearson")
    estimate    p.value statistic  n gp  Method
1 -0.2314476 0.03202051 -2.180458 89  3 pearson
pcor.test(FSFSCT1$SSSDISTotal, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "spearman")
    estimate   p.value  statistic  n gp   Method
1 -0.1013222 0.3532696 -0.9334373 89  3 spearman
pcor.test(FSFSCT1$SSSThrilTotal, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "spearman")
     estimate   p.value  statistic  n gp   Method
1 -0.05390731 0.6220414 -0.4947881 89  3 spearman

Social Stressor

## Heart Rate 
#SRP

pcor.test(SSFHRT1$SRPTotalScore, SSFHRT1$SSHRCombAUCi, list(SSFHRT1$Female, SSFHRT1$White, SSFHRT1$Age),method = "pearson")
   estimate   p.value statistic  n gp  Method
1 0.1376357 0.3970383 0.8565954 43  3 pearson
pcor.test(SSFHRT1$SRPIPMTotal, SSFHRT1$SSHRCombAUCi, list(SSFHRT1$Female, SSFHRT1$White, SSFHRT1$Age), method = "pearson")
   estimate   p.value statistic  n gp  Method
1 0.1659895 0.3060039  1.037622 43  3 pearson
pcor.test(SSFHRT1$SRPCATotal, SSFHRT1$SSHRCombAUCi, list(SSFHRT1$Female, SSFHRT1$White, SSFHRT1$Age), method = "pearson")
   estimate   p.value statistic  n gp  Method
1 0.1404568 0.3873382 0.8745029 43  3 pearson
pcor.test(SSFHRT1$SRPELSTotal, SSFHRT1$SSHRCombAUCi, list(SSFHRT1$Female, SSFHRT1$White, SSFHRT1$Age), method = "pearson")
     estimate   p.value  statistic  n gp  Method
1 -0.04491003 0.7831872 -0.2771236 43  3 pearson
pcor.test(SSFHRT1$SRPASBTotal, SSFHRT1$SSHRCombAUCi, list(SSFHRT1$Female, SSFHRT1$White, SSFHRT1$Age), method = "spearman")
   estimate   p.value statistic  n gp   Method
1 0.1294682 0.4259022 0.8048698 43  3 spearman
# ICU

pcor.test(SSFHRT1$ICUTotScore, SSFHRT1$SSHRCombAUCi, list(SSFHRT1$Female, SSFHRT1$White, SSFHRT1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 -0.2108599 0.1915361 -1.329725 43  3 spearman
pcor.test(SSFHRT1$ICUUncareTotalScore, SSFHRT1$SSHRCombAUCi, list(SSFHRT1$Female, SSFHRT1$White, SSFHRT1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 -0.2369544 0.1409755 -1.503504 43  3 spearman
pcor.test(SSFHRT1$ICUUnemoTotal, SSFHRT1$SSHRCombAUCi, list(SSFHRT1$Female, SSFHRT1$White, SSFHRT1$Age), method = "spearman")
    estimate   p.value  statistic  n gp   Method
1 -0.0830544 0.6103967 -0.5137567 43  3 spearman
# Lev

pcor.test(SSFHRT1$LevTotalScore, SSFHRT1$SSHRCombAUCi, list(SSFHRT1$Female, SSFHRT1$White, SSFHRT1$Age), method = "pearson")
     estimate   p.value  statistic  n gp  Method
1 -0.07698959 0.6367937 -0.4760085 43  3 pearson
pcor.test(SSFHRT1$LevPrimTotalScore, SSFHRT1$SSHRCombAUCi, list(SSFHRT1$Female, SSFHRT1$White, SSFHRT1$Age), method = "spearman")
     estimate   p.value statistic  n gp   Method
1 -0.03286873 0.8404308 -0.202726 43  3 spearman
pcor.test(SSFHRT1$LevSecTotalScore, SSFHRT1$SSHRCombAUCi, list(SSFHRT1$Female, SSFHRT1$White, SSFHRT1$Age), method = "pearson")
   estimate  p.value  statistic  n gp  Method
1 -0.115111 0.479382 -0.7143406 43  3 pearson
# SSS 

pcor.test(SSFHRT1$SSSTotalScore, SSFHRT1$SSHRCombAUCi, list(SSFHRT1$Female, SSFHRT1$White, SSFHRT1$Age), method = "pearson")
     estimate  p.value  statistic  n gp  Method
1 -0.08424336 0.605277 -0.5211636 43  3 pearson
pcor.test(SSFHRT1$SSSDISTotal, SSFHRT1$SSHRCombAUCi, list(SSFHRT1$Female, SSFHRT1$White, SSFHRT1$Age), method = "spearman")
    estimate   p.value  statistic  n gp   Method
1 -0.1192701 0.4635381 -0.7405163 43  3 spearman
pcor.test(SSFHRT1$SSSThrilTotal, SSFHRT1$SSHRCombAUCi, list(SSFHRT1$Female, SSFHRT1$White, SSFHRT1$Age), method = "spearman")
     estimate   p.value  statistic  n gp   Method
1 0.003827218 0.9813009 0.02359273 43  3 spearman
## Skin Conductance 

#SRP

pcor.test(SSFSCT1$SRPTotalScore, SSFSCT1$SSSCCombAUCi, list(SSFSCT1$Female, SSFSCT1$White, SSFSCT1$Age),method = "pearson")
    estimate   p.value statistic  n gp  Method
1 -0.2028813 0.2218515 -1.243141 41  3 pearson
pcor.test(SSFSCT1$SRPIPMTotal, SSFSCT1$SSSCCombAUCi, list(SSFSCT1$Female, SSFSCT1$White, SSFSCT1$Age), method = "pearson")
    estimate   p.value statistic  n gp  Method
1 -0.2619829 0.1120793 -1.628787 41  3 pearson
pcor.test(SSFSCT1$SRPCATotal, SSFSCT1$SSSCCombAUCi, list(SSFSCT1$Female, SSFSCT1$White, SSFSCT1$Age), method = "pearson")
    estimate    p.value statistic  n gp  Method
1 -0.3356007 0.03941387 -2.137574 41  3 pearson
pcor.test(SSFSCT1$SRPELSTotal, SSFSCT1$SSSCCombAUCi, list(SSFSCT1$Female, SSFSCT1$White, SSFSCT1$Age), method = "pearson")
     estimate   p.value   statistic  n gp  Method
1 -0.01299652 0.9382713 -0.07798569 41  3 pearson
pcor.test(SSFSCT1$SRPASBTotal, SSFSCT1$SSSCCombAUCi, list(SSFSCT1$Female, SSFSCT1$White, SSFSCT1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 0.05335392 0.7503821 0.3205801 41  3 spearman
# ICU

pcor.test(SSFSCT1$ICUTotScore, SSFSCT1$SSSCCombAUCi, list(SSFSCT1$Female, SSFSCT1$White, SSFSCT1$Age), method = "spearman")
    estimate   p.value  statistic  n gp   Method
1 -0.1212235 0.4684587 -0.7327446 41  3 spearman
pcor.test(SSFSCT1$ICUUncareTotalScore, SSFSCT1$SSSCCombAUCi, list(SSFSCT1$Female, SSFSCT1$White, SSFSCT1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 -0.1803143 0.2786704 -1.099915 41  3 spearman
pcor.test(SSFSCT1$ICUUnemoTotal, SSFSCT1$SSSCCombAUCi, list(SSFSCT1$Female, SSFSCT1$White, SSFSCT1$Age), method = "spearman")
     estimate  p.value  statistic  n gp   Method
1 -0.09204918 0.582564 -0.5546499 41  3 spearman
# Lev

pcor.test(SSFSCT1$LevTotalScore, SSFSCT1$SSSCCombAUCi, list(SSFSCT1$Female, SSFSCT1$White, SSFSCT1$Age), method = "pearson")
     estimate   p.value  statistic  n gp  Method
1 -0.03624328 0.8289674 -0.2176026 41  3 pearson
pcor.test(SSFSCT1$LevPrimTotalScore, SSFSCT1$SSSCCombAUCi, list(SSFSCT1$Female, SSFSCT1$White, SSFSCT1$Age), method = "spearman")
     estimate   p.value  statistic  n gp   Method
1 -0.08813383 0.5987698 -0.5308688 41  3 spearman
pcor.test(SSFSCT1$LevSecTotalScore, SSFSCT1$SSSCCombAUCi, list(SSFSCT1$Female, SSFSCT1$White, SSFSCT1$Age), method = "pearson")
  estimate   p.value statistic  n gp  Method
1 0.047186 0.7784695 0.2834317 41  3 pearson
# SSS 

pcor.test(SSFSCT1$SSSTotalScore, SSFSCT1$SSSCCombAUCi, list(SSFSCT1$Female, SSFSCT1$White, SSFSCT1$Age), method = "pearson")
     estimate   p.value  statistic  n gp  Method
1 -0.07041353 0.6744266 -0.4235325 41  3 pearson
pcor.test(SSFSCT1$SSSDISTotal, SSFSCT1$SSSCCombAUCi, list(SSFSCT1$Female, SSFSCT1$White, SSFSCT1$Age), method = "spearman")
    estimate  p.value statistic  n gp   Method
1 -0.2617748 0.112375 -1.627398 41  3 spearman
pcor.test(SSFSCT1$SSSThrilTotal, SSFSCT1$SSSCCombAUCi, list(SSFSCT1$Female, SSFSCT1$White, SSFSCT1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 0.09125302 0.5858434 0.5498121 41  3 spearman

Table 3 (Partial Correlations for CD)

This table contain the CD measures will partialing the correlations for gender, race, and age.

Countdown

## HR Signaled 

#SRP

pcor.test(CDFHRT1$SRPTotalScore, CDFHRT1$CDHeartSignal12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age),method = "pearson")
    estimate   p.value  statistic  n gp  Method
1 -0.1478284 0.3268744 -0.9914763 49  3 pearson
pcor.test(CDFHRT1$SRPIPMTotal, CDFHRT1$CDHeartSignal12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson")
    estimate   p.value statistic  n gp  Method
1 -0.1936862 0.1971393 -1.309567 49  3 pearson
pcor.test(CDFHRT1$SRPCATotal, CDFHRT1$CDHeartSignal12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson")
    estimate   p.value statistic  n gp  Method
1 -0.1521182 0.3128734 -1.020919 49  3 pearson
pcor.test(CDFHRT1$SRPELSTotal, CDFHRT1$CDHeartSignal12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson")
     estimate   p.value statistic  n gp  Method
1 -0.03186352 0.8335001 -0.211466 49  3 pearson
pcor.test(CDFHRT1$SRPASBTotal, CDFHRT1$CDHeartSignal12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 -0.2259082 0.1311433 -1.538272 49  3 spearman
# ICU

pcor.test(CDFHRT1$ICUTotScore, CDFHRT1$CDHeartSignal12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman")
    estimate    p.value statistic  n gp   Method
1 -0.2789601 0.06046473 -1.926905 49  3 spearman
pcor.test(CDFHRT1$ICUUncareTotalScore, CDFHRT1$CDHeartSignal12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman")
    estimate   p.value  statistic  n gp   Method
1 -0.1337789 0.3754259 -0.8954376 49  3 spearman
pcor.test(CDFHRT1$ICUUnemoTotal, CDFHRT1$CDHeartSignal12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman")
    estimate    p.value statistic  n gp   Method
1 -0.3724346 0.01080672 -2.661957 49  3 spearman
# Lev

pcor.test(CDFHRT1$LevTotalScore, CDFHRT1$CDHeartSignal12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson")
    estimate   p.value  statistic  n gp  Method
1 -0.1156638 0.4440005 -0.7724108 49  3 pearson
pcor.test(CDFHRT1$LevPrimTotalScore, CDFHRT1$CDHeartSignal12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 -0.1938291 0.1968026 -1.310571 49  3 spearman
pcor.test(CDFHRT1$LevSecTotalScore, CDFHRT1$CDHeartSignal12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson")
     estimate   p.value   statistic  n gp  Method
1 -0.01422392 0.9252513 -0.09436033 49  3 pearson
# SSS 

pcor.test(CDFHRT1$SSSTotalScore, CDFHRT1$CDHeartSignal12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson")
     estimate   p.value  statistic  n gp  Method
1 -0.03521542 0.8162728 -0.2337376 49  3 pearson
pcor.test(CDFHRT1$SSSDISTotal, CDFHRT1$CDHeartSignal12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman")
    estimate   p.value  statistic  n gp   Method
1 0.01171918 0.9383861 0.07774159 49  3 spearman
pcor.test(CDFHRT1$SSSThrilTotal, CDFHRT1$CDHeartSignal12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 0.02108854 0.8893643 0.1399167 49  3 spearman
## HR Unsignaled 

#SRP

pcor.test(CDFHRT1$SRPTotalScore, CDFHRT1$CDHeartUnsig12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age),method = "pearson")
   estimate   p.value statistic  n gp  Method
1 0.1249296 0.4080993 0.8352325 49  3 pearson
pcor.test(CDFHRT1$SRPIPMTotal, CDFHRT1$CDHeartUnsig12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson")
     estimate   p.value  statistic  n gp  Method
1 -0.03193856 0.8331135 -0.2119646 49  3 pearson
pcor.test(CDFHRT1$SRPCATotal, CDFHRT1$CDHeartUnsig12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson")
   estimate    p.value statistic  n gp  Method
1 0.2678968 0.07185722  1.844445 49  3 pearson
pcor.test(CDFHRT1$SRPELSTotal, CDFHRT1$CDHeartUnsig12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson")
   estimate   p.value statistic  n gp  Method
1 0.1175145 0.4366937  0.784942 49  3 pearson
pcor.test(CDFHRT1$SRPASBTotal, CDFHRT1$CDHeartUnsig12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 0.09002456 0.5518548   0.59959 49  3 spearman
# ICU

pcor.test(CDFHRT1$ICUTotScore, CDFHRT1$CDHeartUnsig12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 0.05818488 0.7009085 0.3866098 49  3 spearman
pcor.test(CDFHRT1$ICUUncareTotalScore, CDFHRT1$CDHeartUnsig12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman")
    estimate   p.value  statistic  n gp   Method
1 -0.1256487 0.4053845 -0.8401175 49  3 spearman
pcor.test(CDFHRT1$ICUUnemoTotal, CDFHRT1$CDHeartUnsig12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman")
   estimate  p.value statistic  n gp   Method
1 0.1743482 0.246519  1.174483 49  3 spearman
# Lev

pcor.test(CDFHRT1$LevTotalScore, CDFHRT1$CDHeartUnsig12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson")
     estimate  p.value  statistic  n gp  Method
1 -0.04795238 0.751653 -0.3184464 49  3 pearson
pcor.test(CDFHRT1$LevPrimTotalScore, CDFHRT1$CDHeartUnsig12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman")
   estimate   p.value  statistic  n gp   Method
1 -0.107369 0.4775626 -0.7163463 49  3 spearman
pcor.test(CDFHRT1$LevSecTotalScore, CDFHRT1$CDHeartUnsig12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson")
     estimate   p.value  statistic  n gp  Method
1 -0.05017524 0.7405324 -0.3332446 49  3 pearson
# SSS 

pcor.test(CDFHRT1$SSSTotalScore, CDFHRT1$CDHeartUnsig12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson")
    estimate   p.value statistic  n gp  Method
1 0.08864951 0.5579703 0.5903587 49  3 pearson
pcor.test(CDFHRT1$SSSDISTotal, CDFHRT1$CDHeartUnsig12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 0.01897036 0.9004178 0.1258578 49  3 spearman
pcor.test(CDFHRT1$SSSThrilTotal, CDFHRT1$CDHeartUnsig12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 0.02568922 0.8654304 0.1704593 49  3 spearman
## SC signaled 



#SRP

pcor.test(CDFSCT1$SRPTotalScore, CDFSCT1$CDSkinSignal12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age),method = "pearson")
    estimate   p.value  statistic  n gp  Method
1 -0.1117955 0.4646937 -0.7377165 48  3 pearson
pcor.test(CDFSCT1$SRPIPMTotal, CDFSCT1$CDSkinSignal12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson")
    estimate   p.value statistic  n gp  Method
1 -0.2965734 0.0479008 -2.036378 48  3 pearson
pcor.test(CDFSCT1$SRPCATotal, CDFSCT1$CDSkinSignal12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson")
    estimate   p.value statistic  n gp  Method
1 0.04198522 0.7842071 0.2755585 48  3 pearson
pcor.test(CDFSCT1$SRPELSTotal, CDFSCT1$CDSkinSignal12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson")
     estimate   p.value statistic  n gp  Method
1 -0.09491681 0.5351217 -0.625234 48  3 pearson
pcor.test(CDFSCT1$SRPASBTotal, CDFSCT1$CDSkinSignal12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman")
   estimate   p.value statistic  n gp   Method
1 0.1301221 0.3942403 0.8605841 48  3 spearman
# ICU

pcor.test(CDFSCT1$ICUTotScore, CDFSCT1$CDSkinSignal12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman")
    estimate   p.value  statistic  n gp   Method
1 -0.1275661 0.4036737 -0.8433972 48  3 spearman
pcor.test(CDFSCT1$ICUUncareTotalScore, CDFSCT1$CDSkinSignal12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman")
    estimate   p.value  statistic  n gp   Method
1 -0.1011526 0.5085119 -0.6667215 48  3 spearman
pcor.test(CDFSCT1$ICUUnemoTotal, CDFSCT1$CDSkinSignal12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman")
     estimate   p.value  statistic  n gp   Method
1 -0.08455553 0.5807807 -0.5564605 48  3 spearman
# Lev

pcor.test(CDFSCT1$LevTotalScore, CDFSCT1$CDSkinSignal12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson")
    estimate   p.value  statistic  n gp  Method
1 -0.1471785 0.3346509 -0.9757399 48  3 pearson
pcor.test(CDFSCT1$LevPrimTotalScore, CDFSCT1$CDSkinSignal12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman")
     estimate   p.value  statistic  n gp   Method
1 -0.09652675 0.5281874 -0.6359378 48  3 spearman
pcor.test(CDFSCT1$LevSecTotalScore, CDFSCT1$CDSkinSignal12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson")
    estimate   p.value statistic  n gp  Method
1 -0.1639674 0.2818021 -1.089958 48  3 pearson
# SSS 

pcor.test(CDFSCT1$SSSTotalScore, CDFSCT1$CDSkinSignal12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson")
    estimate   p.value  statistic  n gp  Method
1 -0.1398108 0.3596685 -0.9258943 48  3 pearson
pcor.test(CDFSCT1$SSSDISTotal, CDFSCT1$CDSkinSignal12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman")
    estimate   p.value  statistic  n gp   Method
1 0.00643533 0.9665345 0.04220015 48  3 spearman
pcor.test(CDFSCT1$SSSThrilTotal, CDFSCT1$CDSkinSignal12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman")
     estimate   p.value  statistic  n gp   Method
1 -0.03004106 0.8446925 -0.1970814 48  3 spearman
## SC Unsignaled 

#SRP

pcor.test(CDFSCT1$SRPTotalScore, CDFSCT1$CDSkinUnsig12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age),method = "pearson")
    estimate   p.value statistic  n gp  Method
1 0.01962957 0.8981601 0.1287445 48  3 pearson
pcor.test(CDFSCT1$SRPIPMTotal, CDFSCT1$CDSkinUnsig12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson")
     estimate  p.value   statistic  n gp  Method
1 -0.01057337 0.945042 -0.06933808 48  3 pearson
pcor.test(CDFSCT1$SRPCATotal, CDFSCT1$CDSkinUnsig12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson")
     estimate   p.value  statistic  n gp  Method
1 -0.05179782 0.7354239 -0.3401176 48  3 pearson
pcor.test(CDFSCT1$SRPELSTotal, CDFSCT1$CDSkinUnsig12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson")
    estimate   p.value statistic  n gp  Method
1 0.05571898 0.7162024 0.3659423 48  3 pearson
pcor.test(CDFSCT1$SRPASBTotal, CDFSCT1$CDSkinUnsig12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman")
     estimate  p.value  statistic  n gp   Method
1 -0.08919825 0.560105 -0.5872529 48  3 spearman
# ICU

pcor.test(CDFSCT1$ICUTotScore, CDFSCT1$CDSkinUnsig12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman")
     estimate   p.value  statistic  n gp   Method
1 -0.09137439 0.5505339 -0.6016991 48  3 spearman
pcor.test(CDFSCT1$ICUUncareTotalScore, CDFSCT1$CDSkinUnsig12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman")
      estimate   p.value  statistic  n gp   Method
1 -0.005551595 0.9711281 -0.0364048 48  3 spearman
pcor.test(CDFSCT1$ICUUnemoTotal, CDFSCT1$CDSkinUnsig12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman")
     estimate   p.value   statistic  n gp   Method
1 -0.01403822 0.9270749 -0.09206384 48  3 spearman
# Lev

pcor.test(CDFSCT1$LevTotalScore, CDFSCT1$CDSkinUnsig12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson")
    estimate   p.value statistic  n gp  Method
1 -0.1964881 0.1957882 -1.314075 48  3 pearson
pcor.test(CDFSCT1$LevPrimTotalScore, CDFSCT1$CDSkinUnsig12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman")
   estimate   p.value  statistic  n gp   Method
1 -0.139363 0.3612245 -0.9228702 48  3 spearman
pcor.test(CDFSCT1$LevSecTotalScore, CDFSCT1$CDSkinUnsig12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson")
     estimate  p.value  statistic  n gp  Method
1 -0.05860487 0.702165 -0.3849595 48  3 pearson
# SSS 

pcor.test(CDFSCT1$SSSTotalScore, CDFSCT1$CDSkinUnsig12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson")
    estimate   p.value statistic  n gp  Method
1 0.01821874 0.9054452 0.1194881 48  3 pearson
pcor.test(CDFSCT1$SSSDISTotal, CDFSCT1$CDSkinUnsig12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman")
     estimate   p.value  statistic  n gp   Method
1 -0.07234514 0.6367348 -0.4756452 48  3 spearman
pcor.test(CDFSCT1$SSSThrilTotal, CDFSCT1$CDSkinUnsig12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman")
  estimate   p.value statistic  n gp   Method
1 0.073025 0.6335622 0.4801389 48  3 spearman
Recovery Measures Partials

Heart Rate

## HR Signaled 

#SRP

pcor.test(CDFHRT1$SRPTotalScore, CDFHRT1$HrSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age),method = "pearson")
     estimate  p.value  statistic  n gp  Method
1 -0.05930941 0.695405 -0.3941079 49  3 pearson
pcor.test(CDFHRT1$SRPIPMTotal, CDFHRT1$HrSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson")
    estimate   p.value  statistic  n gp  Method
1 -0.1146755 0.4479296 -0.7657227 49  3 pearson
pcor.test(CDFHRT1$SRPCATotal, CDFHRT1$HrSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson")
    estimate  p.value statistic  n gp  Method
1 -0.1527993 0.310686 -1.025599 49  3 pearson
pcor.test(CDFHRT1$SRPELSTotal, CDFHRT1$HrSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson")
     estimate   p.value  statistic  n gp  Method
1 -0.02160899 0.8866515 -0.1433713 49  3 pearson
pcor.test(CDFHRT1$SRPASBTotal, CDFHRT1$HrSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman")
   estimate  p.value statistic  n gp   Method
1 0.1370913 0.363609 0.9180286 49  3 spearman
# ICU

pcor.test(CDFHRT1$ICUTotScore, CDFHRT1$HrSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 -0.1940939 0.1961797 -1.312432 49  3 spearman
pcor.test(CDFHRT1$ICUUncareTotalScore, CDFHRT1$HrSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman")
    estimate   p.value  statistic  n gp   Method
1 0.01226065 0.9355451 0.08133407 49  3 spearman
pcor.test(CDFHRT1$ICUUnemoTotal, CDFHRT1$HrSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman")
     estimate   p.value  statistic  n gp   Method
1 -0.06332208 0.6758945 -0.4208758 49  3 spearman
# Lev

pcor.test(CDFHRT1$LevTotalScore, CDFHRT1$HrSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson")
     estimate   p.value  statistic  n gp  Method
1 -0.02641331 0.8616738 -0.1752672 49  3 pearson
pcor.test(CDFHRT1$LevPrimTotalScore, CDFHRT1$HrSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman")
    estimate   p.value  statistic  n gp   Method
1 0.01396095 0.9266295 0.09261548 49  3 spearman
pcor.test(CDFHRT1$LevSecTotalScore, CDFHRT1$HrSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson")
     estimate   p.value  statistic  n gp  Method
1 -0.01672586 0.9121508 -0.1109623 49  3 pearson
# SSS 

pcor.test(CDFHRT1$SSSTotalScore, CDFHRT1$HrSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson")
      estimate   p.value   statistic  n gp  Method
1 -0.008305349 0.9563135 -0.05509335 49  3 pearson
pcor.test(CDFHRT1$SSSDISTotal, CDFHRT1$HrSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 0.07393605 0.6253178 0.4917823 49  3 spearman
pcor.test(CDFHRT1$SSSThrilTotal, CDFHRT1$HrSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 0.06595398 0.6632098 0.4384439 49  3 spearman
## HR Unsignaled 

#SRP

pcor.test(CDFHRT1$SRPTotalScore, CDFHRT1$HrUnSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age),method = "pearson")
    estimate  p.value statistic  n gp  Method
1 -0.1963923 0.190832 -1.328593 49  3 pearson
pcor.test(CDFHRT1$SRPIPMTotal, CDFHRT1$HrUnSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson")
    estimate   p.value  statistic  n gp  Method
1 -0.1222748 0.4182108 -0.8172113 49  3 pearson
pcor.test(CDFHRT1$SRPCATotal, CDFHRT1$HrUnSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson")
     estimate   p.value  statistic  n gp  Method
1 -0.09021249 0.5510215 -0.6008519 49  3 pearson
pcor.test(CDFHRT1$SRPELSTotal, CDFHRT1$HrUnSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson")
    estimate    p.value statistic  n gp  Method
1 -0.3093815 0.03641917 -2.158085 49  3 pearson
pcor.test(CDFHRT1$SRPASBTotal, CDFHRT1$HrUnSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman")
     estimate   p.value  statistic  n gp   Method
1 -0.05705782 0.7064396 -0.3790964 49  3 spearman
# ICU

pcor.test(CDFHRT1$ICUTotScore, CDFHRT1$HrUnSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman")
    estimate  p.value  statistic  n gp   Method
1 -0.1228591 0.415973 -0.8211764 49  3 spearman
pcor.test(CDFHRT1$ICUUncareTotalScore, CDFHRT1$HrUnSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 -0.2152969 0.1507343 -1.462414 49  3 spearman
pcor.test(CDFHRT1$ICUUnemoTotal, CDFHRT1$HrUnSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman")
     estimate   p.value  statistic  n gp   Method
1 -0.06759574 0.6553439 -0.4494073 49  3 spearman
# Lev

pcor.test(CDFHRT1$LevTotalScore, CDFHRT1$HrUnSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson")
    estimate  p.value statistic  n gp  Method
1 -0.1607727 0.285806 -1.080501 49  3 pearson
pcor.test(CDFHRT1$LevPrimTotalScore, CDFHRT1$HrUnSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 0.08579742 0.5707555 0.5712221 49  3 spearman
pcor.test(CDFHRT1$LevSecTotalScore, CDFHRT1$HrUnSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson")
    estimate    p.value statistic  n gp  Method
1 -0.3226232 0.02875993 -2.260938 49  3 pearson
# SSS 

pcor.test(CDFHRT1$SSSTotalScore, CDFHRT1$HrUnSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson")
    estimate   p.value statistic  n gp  Method
1 0.04999823 0.7414161 0.3320661 49  3 pearson
pcor.test(CDFHRT1$SSSDISTotal, CDFHRT1$HrUnSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman")
   estimate   p.value statistic  n gp   Method
1 0.0875386 0.5629343 0.5829031 49  3 spearman
pcor.test(CDFHRT1$SSSThrilTotal, CDFHRT1$HrUnSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman")
    estimate   p.value  statistic  n gp   Method
1 -0.0323961 0.8307574 -0.2150043 49  3 spearman

Skin Conductance

## SC signaled 



#SRP

pcor.test(CDFSCT1$SRPTotalScore, CDFSCT1$ScSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age),method = "pearson")
    estimate   p.value statistic  n gp  Method
1 -0.2423723 0.1086808 -1.638187 48  3 pearson
pcor.test(CDFSCT1$SRPIPMTotal, CDFSCT1$ScSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson")
   estimate   p.value statistic  n gp  Method
1 -0.196241 0.1963623 -1.312356 48  3 pearson
pcor.test(CDFSCT1$SRPCATotal, CDFSCT1$ScSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson")
     estimate   p.value  statistic  n gp  Method
1 -0.06581811 0.6675135 -0.4325361 48  3 pearson
pcor.test(CDFSCT1$SRPELSTotal, CDFSCT1$ScSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson")
    estimate   p.value statistic  n gp  Method
1 -0.2438939 0.1064111 -1.649119 48  3 pearson
pcor.test(CDFSCT1$SRPASBTotal, CDFSCT1$ScSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman")
    estimate   p.value  statistic  n gp   Method
1 -0.1427012 0.3497218 -0.9454304 48  3 spearman
# ICU

pcor.test(CDFSCT1$ICUTotScore, CDFSCT1$ScSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman")
     estimate   p.value  statistic  n gp   Method
1 -0.08836769 0.5637784 -0.5817415 48  3 spearman
pcor.test(CDFSCT1$ICUUncareTotalScore, CDFSCT1$ScSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman")
     estimate   p.value statistic  n gp   Method
1 -0.06865035 0.6540877 -0.451235 48  3 spearman
pcor.test(CDFSCT1$ICUUnemoTotal, CDFSCT1$ScSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 0.01789238 0.9071315  0.117347 48  3 spearman
# Lev

pcor.test(CDFSCT1$LevTotalScore, CDFSCT1$ScSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson")
    estimate   p.value statistic  n gp  Method
1 -0.1533109 0.3146754 -1.017354 48  3 pearson
pcor.test(CDFSCT1$LevPrimTotalScore, CDFSCT1$ScSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman")
     estimate   p.value  statistic  n gp   Method
1 0.008197381 0.9573788 0.05375563 48  3 spearman
pcor.test(CDFSCT1$LevSecTotalScore, CDFSCT1$ScSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson")
    estimate   p.value statistic  n gp  Method
1 -0.2465958 0.1024704 -1.668565 48  3 pearson
# SSS 

pcor.test(CDFSCT1$SSSTotalScore, CDFSCT1$ScSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson")
     estimate   p.value  statistic  n gp  Method
1 -0.06753204 0.6593763 -0.4438504 48  3 pearson
pcor.test(CDFSCT1$SSSDISTotal, CDFSCT1$ScSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman")
    estimate  p.value statistic  n gp   Method
1 0.08232942 0.590815 0.5417091 48  3 spearman
pcor.test(CDFSCT1$SSSThrilTotal, CDFSCT1$ScSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 0.03133836 0.8380734 0.2056003 48  3 spearman
## SC Unsignaled 

#SRP

pcor.test(CDFSCT1$SRPTotalScore, CDFSCT1$ScUnSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age),method = "pearson")
   estimate   p.value statistic  n gp  Method
1 -0.233414 0.1227994 -1.574078 48  3 pearson
pcor.test(CDFSCT1$SRPIPMTotal, CDFSCT1$ScUnSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson")
    estimate   p.value  statistic  n gp  Method
1 -0.1284775 0.4002952 -0.8495237 48  3 pearson
pcor.test(CDFSCT1$SRPCATotal, CDFSCT1$ScUnSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson")
     estimate   p.value  statistic  n gp  Method
1 -0.02519517 0.8695074 -0.1652682 48  3 pearson
pcor.test(CDFSCT1$SRPELSTotal, CDFSCT1$ScUnSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson")
    estimate    p.value statistic  n gp  Method
1 -0.2614933 0.08271926 -1.776541 48  3 pearson
pcor.test(CDFSCT1$SRPASBTotal, CDFSCT1$ScUnSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman")
    estimate    p.value statistic  n gp   Method
1 -0.2506389 0.09678388  -1.69774 48  3 spearman
# ICU

pcor.test(CDFSCT1$ICUTotScore, CDFSCT1$ScUnSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman")
    estimate   p.value  statistic  n gp   Method
1 -0.1101793 0.4712145 -0.7269194 48  3 spearman
pcor.test(CDFSCT1$ICUUncareTotalScore, CDFSCT1$ScUnSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 -0.1276759 0.4032659 -0.844135 48  3 spearman
pcor.test(CDFSCT1$ICUUnemoTotal, CDFSCT1$ScUnSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 0.01666282 0.9134883 0.1092806 48  3 spearman
# Lev

pcor.test(CDFSCT1$LevTotalScore, CDFSCT1$ScUnSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson")
     estimate   p.value  statistic  n gp  Method
1 -0.09580305 0.5312991 -0.6311256 48  3 pearson
pcor.test(CDFSCT1$LevPrimTotalScore, CDFSCT1$ScUnSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman")
    estimate  p.value  statistic  n gp   Method
1 0.01120758 0.941751 0.07349764 48  3 spearman
pcor.test(CDFSCT1$LevSecTotalScore, CDFSCT1$ScUnSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson")
    estimate p.value statistic  n gp  Method
1 -0.1985125 0.19113 -1.328166 48  3 pearson
# SSS 

pcor.test(CDFSCT1$SSSTotalScore, CDFSCT1$ScUnSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson")
    estimate   p.value statistic  n gp  Method
1 0.03311082 0.8290478 0.2172413 48  3 pearson
pcor.test(CDFSCT1$SSSDISTotal, CDFSCT1$ScUnSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman")
   estimate   p.value statistic  n gp   Method
1 0.2239739 0.1391279   1.50698 48  3 spearman
pcor.test(CDFSCT1$SSSThrilTotal, CDFSCT1$ScUnSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman")
   estimate   p.value statistic  n gp   Method
1 0.1659117 0.2760564  1.103246 48  3 spearman

Figures

Code for Fig 1

HR

pacman::p_load(ggtext, fontawesome, showtext, sysfonts, patchwork, ggrepel)

font_add_google("Roboto")
showtext_auto()
custom_labels <- c(0, 25, 50, 75, 100, 125, 150, 175, 200, 225)

HRViz <- ggplot(data12, aes(x = time, y = value)) +
  geom_line(color = "#A7C7E7") +
  geom_point(color = "#FF964F", size = 1) +
  geom_label_repel(data = subset(data12, time == 24),
                   aes(label = "Speech Task Begins"), 
                   point.padding = 1,
                   nudge_x = 0.7, 
                   nudge_y = -2,
                   arrow = arrow(length = unit(0.015, "npc")),
                   family = "Roboto",
                   size = 3
                   ) +
  labs(title = "Social Stressor Task: Heart Rate",
       x = "Time (Seconds)",
       y = "Heart Rate (BPM)") +
  theme_minimal() +
  theme(plot.title = element_text(family = "Roboto",
                                  size = 12,
                                  hjust = 0.5, 
                                  face = "bold", 
                                  margin = margin(b = 6)),
        axis.title.y = element_text(family = "Roboto",
                                    size = 10),
        axis.title.x = element_text(family = "Roboto",
                                    size = 10)) +
  scale_x_continuous(breaks = seq(0, max(data12$time), by = 5),
                     labels = custom_labels) +
  scale_y_continuous(expand = expansion(mult = c(0.1, 0.1)),
                     breaks = seq(floor(min(data12$value)), 
                                  ceiling(max(data12$value)),
                                  by = 1)) +
  coord_cartesian(ylim = c((min(data12$value)) - 0.15, (max(data12$value)) + 0.15)) 

Skin Conductance

custom_labels <- c(0, 25, 50, 75, 100, 125, 150, 175, 200, 225)

SCViz <- ggplot(data123, aes(x = time, 
                             y = value)) +
  geom_line(color = "#A7C7E7") +
  geom_point(color = "#FF964F", 
             size = 1) +
  geom_label_repel(data = subset(data123, 
                                 time == 24),
                   aes(label = "Speech Task Begins"), 
                   point.padding = 1,
                   nudge_x = 1, 
                   nudge_y = -0.3,
                   arrow = arrow(length = unit(0.015, "npc")),
                   family = "Roboto",
                   size = 3
                   ) +
  labs(title = "Social Stressor Task: Skin Conductance",
       x = "Time (Seconds)",
       y = "Skin Conductance Level (μS)") +
  theme_minimal() +
  theme(plot.title = element_text(family = "Roboto",
                                  size = 12,
                                  hjust = 0.5, 
                                  face = "bold", 
                                  margin = margin(b = 6)),
        axis.title.y = element_text(family = "Roboto",
                                    size = 10),
        axis.title.x = element_text(family = "Roboto",
                                    size = 10)
        ) +
  scale_x_continuous(breaks = seq(0, max(data123$time), by = 5),
                     labels = custom_labels)

Plots (Fig 1)

HRSCStacked <- HRViz / SCViz

HRSCStacked

Code for Fig 2

HR

CDHRVizStack <- ggplot(CDGraphHR,
                  aes(x = time, 
                      y = value, 
                      group = group,
                      color = group)) +
  geom_line() +
  geom_label_repel(
    data = subset(CDGraphHR, time == 6 & group == "Signaled"),
    aes(label = "Countdown Begins"), 
    point.padding = 1,
    nudge_x = 4.0, 
    nudge_y = 0.6,
    arrow = arrow(length = unit(0.015, "npc")),
    family = "Roboto",
    color = "black",
    size = 2,
    show.legend = FALSE
  ) +
    geom_label_repel(
    data = subset(CDGraphHR, time == 18 & group == "Signaled"),
    aes(label = "Noise Blast"), 
    point.padding = 1,
    nudge_x = 4, 
    nudge_y = 0.5,
    arrow = arrow(length = unit(0.015, "npc")),
    family = "Roboto",
    color = "black",
    size = 2,
    show.legend = FALSE
  ) +
  geom_vline(xintercept = 6, linetype = "solid", color = "#F5F5F5") +
  geom_vline(xintercept = 18, linetype = "solid", color = "#F5F5F5") + 
  scale_color_manual(values = c("Signaled" = "#A7C7E7",
                                "Unsignaled" = "#E7BFA7"),
                     labels = c("Signaled", "Unsignaled")) +
  geom_point(color = "#FF964F", size = 1)  +
  labs(title = "Countdown Task: Heart Rate",
       x = "Time (Seconds)",
       y = "Heart Rate (BPM)") +
  theme_minimal() +
  theme(plot.title = element_text(family = "Roboto",
                                  size = 12,
                                  hjust = 0.5, 
                                  face = "bold", 
                                  margin = margin(b = 6)),
        axis.title.y = element_text(family = "Roboto",
                                    size = 10),
        axis.title.x = element_text(family = "Roboto",
                                    size = 10),
        legend.title = element_blank(),  
        legend.text = element_text(family = "Roboto", 
                                   size = 10), 
        legend.position = "top"  
        ) +
  scale_y_continuous(expand = expansion(mult = c(0.1, 0.1)),
                     breaks = seq(floor(min(CDGraphHR$value)), 
                                  ceiling(max(CDGraphHR$value)),
                                  by = 1)) +
  coord_cartesian(ylim = c((min(CDGraphHR$value)) - 0.15, (max(CDGraphHR$value)) + 0.15)) 

SC

CDSCVizStack <- ggplot(CDGraphSC,
                  aes(x = time, 
                      y = value, 
                      group = group,
                      color = group)) +
  geom_line() +
  geom_label_repel(
    data = subset(CDGraphSC, time == 6 & group == "Unsignaled"),
    aes(label = "Countdown Begins"), 
    point.padding = 1,
    nudge_x = 3.2, 
    nudge_y = 0.3,
    arrow = arrow(length = unit(0.015, "npc")),
    family = "Roboto",
    color = "black",
    size = 2,
    show.legend = FALSE
  ) +
    geom_label_repel(
    data = subset(CDGraphSC, time == 18 & group == "Unsignaled"),
    aes(label = "Noise Blast"), 
    point.padding = 1,
    nudge_x = -3, 
    nudge_y = 0.3,
    arrow = arrow(length = unit(0.015, "npc")),
    family = "Roboto",
    color = "black",
    size = 2,
    show.legend = FALSE
  ) +
  geom_vline(xintercept = 6, linetype = "solid", color = "#F5F5F5") +
  geom_vline(xintercept = 18, linetype = "solid", color = "#F5F5F5") + 
  scale_color_manual(values = c("Signaled" = "#A7C7E7",
                                "Unsignaled" = "#E7BFA7"),
                     labels = c("Signaled", "Unsignaled")) +
  geom_point(color = "#FF964F", size = 1)  +
  labs(title = "Countdown Task: Skin Conductance Level",
       x = "Time (Seconds)",
       y = "Skin Conductance Level (μS)") +
  theme_minimal() +
  theme(plot.title = element_text(family = "Roboto",
                                  size = 12,
                                  hjust = 0.5, 
                                  face = "bold", 
                                  margin = margin(b = 6)),
        axis.title.y = element_text(family = "Roboto",
                                    size = 10),
        axis.title.x = element_text(family = "Roboto",
                                    size = 10),
        legend.title = element_blank(),  
        legend.text = element_text(family = "Roboto", 
                                   size = 10), 
        legend.position = "top"  
        ) 

Plots (Fig 2)

CDHRSCStacked <- CDHRVizStack / CDSCVizStack

CDHRSCStacked